# Semeru volcano, Indonesia: measuring hazard, exposure and response of densely populated neighbourhoods facing persistent volcanic threats

Jean-Claude Thouret, Marie Taillandier, Emeline Wavelet, Nourddine

Azzaoui, Olivier Santoni, Boedi Tjahjono

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## ► To cite this version:

Jean-Claude Thouret, Marie Taillandier, Emeline Wavelet, Nourddine Azzaoui, Olivier Santoni, et al.. Semeru volcano, Indonesia: measuring hazard, exposure and response of densely populated neighbourhoods facing persistent volcanic threats. Natural Hazards, 2023, 117 (2), pp.1405-1453. 10.1007/s11069-023-05910-5 . hal-04261504

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# Natural Hazards

## Semeru volcano, Indonesia: Measuring hazard, exposure and response of densely populated neighbourhoods facing persistent volcanic threats

### --Manuscript Draft--

<table><tr><td>Manuscript Number:</td><td>NHAZ-D-22-01507R1</td></tr><tr><td>Full Title:</td><td>Semeru volcano, Indonesia: Measuring hazard, exposure and response of densely populated neighbourhoods facing persistent volcanic threats</td></tr><tr><td>Article Type:</td><td>Manuscript</td></tr><tr><td>Keywords:</td><td>human exposure; statistics; neighbourhood, volcano; Semeru; Indonesia</td></tr><tr><td>Abstract:</td><td>We studied Semeru, East Java to show the population exposure to volcanic threats from its persistent, daily eruptive activity which endangers at least 50,000 of the 950,000 inhabitants living on the East and SE slopes and ring plain. Surveys, mapping and statistical investigation enabled us to assess the extent of exposure of 145 neighbourhoods (termed blocks) and characterize hazards and response to eruptions in 15 rural villages and small towns. Statistical analyses of datasets of 23 variables (11 of exposure, 7 of hazards, and 5 of response) and their attributes involved three operations: 1. Univariate and bivariate analyses enabled us to explore data and characterize the relationships between 11 variables to compute a multi-component exposure index. 2. Polytomous Logistic Regression (PLR) models selected six optimal exposure variables, suggesting that logistic regression can predict the exposure index for blocks outside the survey area and potentially on any active volcano. 3. Multivariate analyses and Hierarchical Agglomerative Clustering (HAC) distinguished four groups of blocks based on attributes of all variables correlated to the exposure index score. To contribute to disaster risk reduction, the distance/time criterion was applied to access ways and response facilities to highlight remote or blocked blocks in danger of imminent eruption including evacuation. Statistical analysis of optimal variables from local scale surveys can help identify neighbourhoods where disaster risk mitigation requires improvement.</td></tr><tr><td>Response to Reviewers:</td><td>Dr. James Goff, Editor in Chief, Natural Hazards Journal<br>We would like to submit the revised article entitled: "Semeru volcano, Indonesia: Measuring hazard, exposure and response of densely populated neighbourhoods facing persistent volcanic threats" to Natural Hazards.<br>In response to your request, we apologize and we thank you for having drawn our attention to the short paragraphs that may be considered as plagiarism and needed to be reworked before peer review. We have revised the third paragraph in sections 1.1, the first and third paragraphs in section 1.2, the second paragraph in section 1.3, and finally the first paragraph in section 1.5. We have shortened and reworked the mentioned lines, indicated their sources and added citations where appropriate. In the case of sections 1.3 and 1.5, we have referred to the publication from which the lines have been borrowed. In fact, we borrowed the content of these lines from our publication "Thouret et al., 2022" (International Journal of Disaster Risk Reduction). We hope that the revision would meet your expectation before peer review.<br>Thank you for your consideration on our manuscript.</td></tr></table>
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1 Semeru volcano, Indonesia: Measuring hazard, exposure and response of densely

2 populated neighbourhoods facing persistent volcanic threats

3

4 Jean-Claude Thouret ¹, Marie Taillandier ², Emeline Wavelet ¹, Nourddine Azzaoui ², Olivier

5 Santoni ³, Boedi Tjahjono ⁴

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7 ¹ Université Clermont-Auvergne, Laboratoire Magmas et Volcans, UMR 6524 CNRS, OPGC

8 et IRD, F 63000 Clermont-Ferrand (j-claude.thouret@uca.fr)

9 ² Université Clermont-Auvergne UCA, Laboratoire de Mathématiques UMR 6566, CNRS,

10 Campus les Cézeaux, 63178 Aubière, France (nourddine.azzaoui@uca.fr)

11 ³ FERDI & Université Clermont Auvergne, CNRS, IRD, CERDI, F-63000 Clermont-Ferrand,

12 France (olivier.santoni@uca.fr)

13 ⁴ IPB University, Faculty of Agriculture, Bogor, Indonesia (boetjah@apps.ipb.ac.id)

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Submitted to Natural Hazards

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20 September 2022

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# ABSTRACT

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We studied Semeru, East Java to show the population exposure to volcanic threats from its persistent, daily eruptive activity which endangers at least 50,000 of the 950,000 inhabitants living on the East and SE slopes and ring plain. Surveys, mapping and statistical investigation enabled us to assess the extent of exposure of 145 neighbourhoods (termed blocks) and characterize hazards and response to eruptions in 15 rural villages and small towns. Statistical analyses of datasets of 23 variables (11 of exposure, 7 of hazards, and 5 of response) and their attributes involved three operations: 1. Univariate and bivariate analyses enabled us to explore data and characterize the relationships between 11 variables to compute a multi-component exposure index. 2. Polytomous Logistic Regression (PLR) models selected six optimal exposure variables, suggesting that logistic regression can predict the exposure index for blocks outside the survey area and potentially on any active volcano. 3. Multivariate analyses and Hierarchical Agglomerative Clustering (HAC) distinguished four groups of blocks based on attributes of all variables correlated to the exposure index score. To contribute to disaster risk reduction, the distance/time criterion was applied to access ways and response facilities to highlight remote or blocked blocks in danger of imminent eruption including evacuation.
Statistical analysis of optimal variables from local scale surveys can help identify neighbourhoods where disaster risk mitigation requires improvement.

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**Key-words:** human exposure; statistics; neighbourhood, volcano; Semeru; Indonesia.

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# 1. Introduction

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## 1.1. Study background and terminology

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The present study is part of the ‘Local Adaptation to Volcanic Risk’ research project, and seeks to understand how and why dense rural communities continuously exposed to persistent volcanic threats can thrive on Semeru’s slopes and ring plain. Another paper explains how Semeru’s communities adjust to, compensate for, and tolerate continuous exposure to persistent volcanic threats (Thouret et al., 2022). Here, based on field surveys, mapping and statistical analyses, we seek to determine the population exposure to persistent volcanic threats by means of a composite index computed at the scale of neighbourhoods (termed blocks) in rural villages and small towns (Fig. 1, ESD Table 1). Using a second group of
variables on volcanic hazards and a third group on access and response, enabled us to rank
and map a range of remote blocks in the case of imminent eruptions.

In this study, we use the international terminology on Disaster Risk Reduction (UNDRR, 2017; UNISDR, 2017) to which the reader is referred. Risk is the product of probability × losses, where the probability is a function of hazard and the losses depend on both exposure and external and internal vulnerability (Aspinall and Blong, 2015). Volcanic risk analysis has focused on cost-benefit evaluation (e.g., Woo, 2015) in particular the case of evacuations preceding imminent eruptions (Jumadi et al., 2018, Lechner and Rouleau, 2019). The concept of risk also includes hazard knowledge, risk perception and resilience among communities living on active volcanoes (Gaillard, 2008; Gaillard and Dibben, 2008; Paton et al., 2008; Lavigne et al. 2008; Donovan, 2010; Donovan et al., 2018). A broader risk concept stems from the appraisal of value systems and beliefs, governance systems and decisions, and political economies (Bakkour et al., 2015).

Exposure and vulnerability are two principal components of risk. Exposure is the location and
spatial distribution of people, buildings, property, infrastructure, networks, lifelines,
production capacities and other tangible human assets within reach of a hazard event
(UNDRR, 2017). In the context of disaster risk reduction, Wisner et al. (2004) defined
vulnerability as "the characteristics of a person or group to anticipate, cope with, resist or
recover from the impact of a natural hazard". Coping capacity is part of the widely used term
of resilience, which is the ability of a community exposed to hazards to resist, absorb,
accommodate to and recover from the effects of a hazard in a timely and efficient manner
(UNISDR, 2008, 2017). Here we focus on exposure, a concept that has often been included in
the vulnerability field; but exposure means being potentially affected by volcanic activity in a
peculiar place, whereas vulnerability is the inability to withstand the effects of a harmful
process. As stated by the First IAVCEI-GVM workshop (2018), "exposure and vulnerability
may be related: exposure quantifies number of people and/or buildings in the area, while
vulnerability is one of the characteristics of the exposed elements that may suffer hazard
impacts".

Studies measuring population exposure to volcanic hazards (Ewert, 2007; Kinvig et al., 2010; Brown et al., 2015a; Nieto-Torres et al., 2021) are relatively few, compared with works on physical exposure of buildings and critical infrastructure (e.g., Lerner-Lam, 2007; Jenkins et al., 2014, 2015; Wilson et al. 2014; Jimenez et al., 2019) and profuse investigations on
vulnerability and risk perception (e.g., Jóhannesdóttir and Gísladóttir, 2010; Zuccaro et al., 2015; Thouret et al., 2014a; Michellier et al., 2020). Yet, exposure, having both spatial and temporal patterns, is a fundamental component of risk analysis, and together with vulnerability, underpins loss. Exposure compels threatened people to adjust to risk through resilience to avoid the dangers of persistent volcanic activity.

## 1.2. Global and other approaches to exposure

We use local-scale surveys and statistical methods for defining exposure around Semeru. The main alternative is the Global approach. Exposure of people to volcanic risk has been the focus of the 'priority countries of the Global Facility for Disaster Reduction and Recovery' (GFDRR) report by Aspinall et al. (2011), This pilot study on volcanic risk in 31 countries presented a method for "measuring the Volcano Population Index (the number of people threatened by each volcano: Ewert and Harpel, 2004) combined with the hazard level of each volcano to quantify population risk". The Population Exposure Index (PEI: Aspinall et al., 2011, later summarized by Brown et al. (2015a) is one of the main indices used in assessing volcanic risk in highly populated areas together with Volcanic Hazard Index (VHI, Brown et al., 2015b), Human Development Index (HDI, UNDP, 2020) and Social Vulnerability Index (SoVI, The Hazards and Vulnerability Research Institute, University of Southern Carolina). VHI is an index rating the hazard level of a volcano based on the recurrence of past eruptions, the average and maximum Volcanic Explosivity Index ratings, and the extent of pyroclastic, mud, or lava flows in the eruptions. The Human Development Index (UNDP, 2020) measures levels of social and economic development based on "four criteria: mean and expected years of schooling, life expectancy at birth, and gross national income per capita". The Social Vulnerability Index to hazards is based on "many socioeconomic, demographic, and built environment variables at the country or district level" (HVRI, University of South Carolina).

The GFDRR report (2011) estimated the numbers of people living within 10 km, 30 km and 100 km of each volcano, which was weighted according to evidence on historical distributions of fatalities within a given distance from the vent. Population numbers were weighted, summed, and assigned to one of seven PEI scores in populated areas (Brown et al., 2015b). The authors estimated population risk for each volcano by "taking the product of the Hazard Level and PEI, and the numerical product was assigned to one of three Population Risk Levels". Following this approach, Semeru volcano, one of the prominent Indonesia's active volcanoes within the GFDRR category A, was assigned a high PEI level 3 with hazard level 3
and a low uncertainty level 1. The method relies on a number of parameters having high uncertainty or that are unknown, which restricts its application to volcanoes with good historical records.

A physical exposure component was introduced in the USGS re-assessment of US dangerous volcanoes. The updated Volcanic Threat Assessment (e.g., Ewert et al., 2018; Mangan et al., 2018) combines 15 hazard factors and 9 exposure factors that describe an individual volcano's hazard potential and the exposure of people and property to hazards. The exposure factors include population within 30 kilometers of the volcano; visitor numbers if it is located in a national park or monument; population beyond 30 kilometers if a far-traveling lahar is a primary hazard; prior eruption fatalities; prior evacuations; aviation impacts; impacts on power and transportation infrastructure; and major developments such as parks. A physical exposure component is also part of Del Negro et al (2020) quantification of lava flow risk on the flanks of Mt. Etna volcano using a GIS-based approach that integrates exposure of elements at stake within the hazard. The total exposure results from a weighted linear combination of four thematic layers, population, buildings, service networks, and land use, the weights of which were calculated using the Analytic Hierarchy Process. Population exposure relied on the density of population for each community normalized by the value obtained for the more populated municipality. Wood and Soulard (2009) proposed a composite exposure index for communities living in lahar-prone hazard zones around Mt. Rainier. They estimate the exposure index on the amount and percentage of six variables: developed land, residents, employees, public venues, dependent-population facilities, and parcel value, to estimate the spatial variations in community exposure.

More recently, Nieto-Torres et al (2021) quantified a multi-component volcanic risk index, which encompasses as many as 41 parameters: 9 parameters for hazard, 9 for exposure, 10 for vulnerability and 13 for resilience. However, the exposure criteria contain only one single human parameter, i.e., the density of population within 5, 10, 30 and 100 km radii from the main crater.

## 1.3. Semeru volcano, setting and rationale for targeting continuous exposure

Semeru, East Java, is Indonesia's highest and the southernmost volcano of the Tengger massif, which includes the Tengger caldera with the active Bromo cone (Fig. 2). The proximal (10 km), medial (30 km) and distal (100 km) areas exposed to the effects of volcanic hazards
rank Semeru as one of the world's most dangerous volcanoes according to the GFDRR report.
It hosts one of the most exposed populations worldwide, as the 100-km circle distance
encompasses Java's second densest populated area (see Freire et al., 2019, their Fig. 9),
including 10 million people of the metropolitan area of Surabaya, Indonesia's second
economic centre and second international airport. The province of East Java with an area of c.
48,000 km² is home to about 40 million people, making it one of the most densely populated,
largely rural areas on Earth with 830 people per km² on average. East Java presents typical
low- to middle-income population at the lower range of the country's HDI (0.59-0.71; BPS
2017) and mean SoVI score (Siagan et al., 2013). Java hosts 58% of Indonesia's population
over < 7% of the country size, i.e., 141 million (2015) among which 10% are exposed to
disaster risk within 30 km from 22 historically active volcanoes.

Semeru is a real concern to civil authorities owing to the combination of daily explosive
activity and a dense population: at least 950,000 people live within a radius of 35 km from the
volcano summit (Thouret et al., 2022). Lahar-related disasters caused >10,000 casualties
during the 20th century alone. The 1909 catastrophic event ranks fourth among the ten
deadliest eruptions in the world between 1900 and 2010, as the 1909 PDCs and lahars killed
at least 5500 people, i.e., 5.5% of all fatalities of the ten world's deadliest eruptions since
1900 (Doocy, 2013). These events created havoc in thriving urban centres located in the
eastern ring plain, in particular Pasirian, Tempeh, and Lumajang, harboring 123,000 people
(as of 2015). Semeru's summit separates the Regencies of Lumajang to the East (1791 km²,
1,036,000 people in 2015) and Malang to the West (3531 km², 2,547,000 people) (Fig. 2;
BPS, 2017). Here we focus on the regency of Lumajang where 621,000 people live within 35
km of the summit, and the SW flank where 317,000 people represent a quarter of Malang's
regency.

The study area hosts a range of areas exposed to deadly or disastrous pyroclastic density
currents (PDCs), tephra fallout on proximal slopes, lahars along valleys as far as 35 km, and a
variety of exposed assets, such as crops, road network, lifelines, factories, trade centres,
religious monuments and touristic facilities in the ring plain. We defined and computed
exposure of population living in 15 *dusun* of 6 districts (*Kecamatan*) with c. 800
inhabitants/km² on average, although the size of rural settlements is highly variable between
hamlets (350 people) and small, mixed rural/urban towns c. 7,500 people (ESD Table 1). The
outlook of many of the towns is still rural as the growth of urban centres (Pronojiwo, Senduro,
Candipuro) has not been accompanied by a parallel growth of industry. Urban centres benefit
from diversity, as they offer a variety of jobs and business such as agricultural trade, wood
industries, traditional crafts, and local tourism. Towns around Semeru mirror other Javan
centres in displaying great socio-economic diversity, which underlies a three-tiered social
hierarchy. Villagers are ethnically Javanese with a significant Madurese minority. Agriculture
and forestry are the main activities occupying 50% of the land and 40% of the workforce.
Trade routes and small manufacturing are growing in towns, but roads and railways remain
poorly developed.

1.4. Semeru's persistent eruptive activity and extensive hazard-prone areas

Eruptive activity, which has been recorded since 1884, is usually mild (VEI 1-2), but
increases every 8 to 11 years (Thouret et al., 2007; Solikhin et al., 2012). The constant activity
includes four eruptive styles:

1. The persistent vulcanian and sometimes strombolian regime consists of short-lived, ash-laden < 0.5 km-high columns several times a day. Small columns usually disperse ash about 4 km around the summit, but all villages can be affected by ash dispersed from 4-6 km-high columns as far as 8 to 12 km mostly to East, SE and NE, while exceptional seasonal winds can blow fine ash as far as 20 km ESE and WSW (Fig. 3).

2. Increased vulcanian/pelean activity every 8 to 11 years produces several kilometer-high eruption columns, ballistic bombs and thick tephra fall around the vent, and dispersed ashfall 40 km downwind. Collapses from crater- and dome-fed, steep lava flows produce block-and-ash flows that travel toward the SE as far as 12 km from the summit, e.g., 2002 and 2020-21.

3. Flank 'aa' lava flows 5 km long erupted on the lower SE and E flanks in 1895 and in 1941-1942, while the crater termed Jenggring-Seloko (Fig. 3) has regularly produced stubby lava flows reaching 1 to 3.3 km along the steep scar open to the SSE over the past 40 years.

4. Highly explosive eruptions are not unknown. Nakada et al. (2019) refer to deposits from relatively large (≥ VEI 3) eruptions from the 3rd to the 11th Centuries as well as thick scoria falls and PDCs that destroyed the temple of the Majapahit Kingdom (13-16th Century) at Candi Jawar (5.5 km SW of the present summit), around the 16th century. The East and South flanks of Semeru together with the East and SE ring plain are the most prone to volcanic hazards where 50,000 to 100,000 people are exposed to the effects of eruptive activity.
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The hazard-zone map (Fig. 3) displays three areas prone to volcanic threats as follows (Thouret et al., 2007, 2014b):

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1) The high and steep summit cone within a circle of 5 km radius is affected by tephra-fall, lava flows and PDCs on the **SE** flank on daily to annual basis. No one lives there, but scores of tourists frequently climb and visit the summit area (5 victims were reported in 2000). New tephra fallout covers the summit cone on a daily basis, while dome-collapse rock avalanches and PDCs propagate annually as far as 4 km through the scar open to the **SE**, but they do not reach villages during the usual volcano “unrest” level 2 on a scale of 4.

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2) The extensive South, **SE** and **ESE** flanks affected by PDCs as far as 12 km and lahars as far as 9 to 18 km from the vent, along which more than 50,000 people now live within 0.5 km of the active rivers (e.g., K. Koboan, K. Bang and K. Kembar, Fig. 3). On 4 December 2021, both margins of the Koboan valley were affected by exceptionally long runout PDCs and hot lahars flowing 16 km down valley and causing more than 50 fatalities (GVP, 2021).

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3) The principal valleys draining the South (K. Glidik, K. Bang and K. Kembar), **SE** (K. Koboan), and East slopes (K. Tengah or Besuk Sat) convey many annual lahars across the ring plain at least 35 km down valley to the Indian Ocean (Thouret et al., 2014b). Post-eruptive lahars are the most frequent hazardous flows that propagate every rainy season along the principal valleys to the South, **SE** and East. Voluminous lahars, exceeding 5 million m³, killed hundreds of people at least five times since 1884; they swept the **SE** and **E** ring plain in 1976, 1981, and devastated the city of Lumajang 35 km East in 1909 (Fig. 1).

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In sum, the most exposed population live along the valleys within 8 to 25 km from the summit. Recorded fatalities due to lahars and PDCs are higher along the principal valley reaches between 9 and 12 km distance (75%). Fatalities due to lahars occurred between 12 and 25 km (22%), the remainder (3%) being located along the > 25 km distal valleys in 1909, 1976 and 1981 (Thouret et al., 2007).

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## 1.5. Location choice and local scale of observation: *Dusun* and neighbourhood

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We studied dense, mostly rural communities located in the exposed areas between 8 and 20 km from the summit of Semeru noting past eruptions and reported fatalities (Fig. 3; Thouret et al., 2007, their Table 1). The primary unit of study was neighbourhood (block) in sub-villages (termed *dusun*) located on the valley margins, terraces and interfluves within 0.5 km from the active channel that used to be affected by lahars and PDCs in the recent past. Locations

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243 included the South (K. Kali= river) Bang, K. Kembar, e.g., 2002-2003), SSE (K. Koboan and
244 C. Lengkong, 1994-1995, 2020-2021), East (K. Tengah/ Besuk Smut, 1981), and the SE and
245 East ring plain (K. Rejali, Mujur) towards the city of Lumajang, affected in 1909 (Fig. 3). We
246 also studied villages farther away from active rivers and the town of Senduro outside the
247 affected valleys to avoid any bias that would stem from the exposed settlements in affected
248 hazard zones (Thouret et al., 2022). The remaining dusun within the reach of light ashfall
249 were selected for the purpose of comparison as they extend beyond active valleys (e.g.,
250 Pasrujambe, Fig. 2) and farther away from the volcano. We examined small rural
251 communities at higher altitude on the west flank that are exposed to frequent tephra-fall
252 within 9 km of the crater (Fig. 2). We also considered mixed rural/urban communities in two
253 towns: Pronojiwo in the vicinity of the active K. Bang and Kembar valleys, and Senduro
254 farther away (17 km) from the volcano and any active river.

255 We selected 15 dusun that belong to six Desa on the SW, S and E slopes of Semeru inside
256 four Kecamatan in the Regency of Lumajang and one in Malang Regency (Table 1, Figs. 2-3;
257 ESD Fig. 1) based on the following rationale:

258 1. Both dusun Blubuk and Karangsuko (Desa Tamansatryan), located between 900 and 1250
259 m asl on the WSW flank 8-9 km from the vent, are exposed to frequent, light tephra-fall from
260 the volcano. Both mid-altitude hamlets exemplify chronic exposure of a population living
261 close to the crater.

262 2. The small town of Pronojiwo (Desa and Kecamatan Pronojiwo) is the largest settlement (c.
263 7,600 people) on the South flank of Semeru between 600 and 700 m asl and at a 11-12 km
264 distance of the summit. The rural suburb to the East of the town near two large rivers (K.
265 Bang and K. Kembar) has regularly been affected by PDCs and annual lahars. On the ENE
266 side of Pronojiwo, the dusun Supit and Supit Timur 9 to 11 km from the vent and between
267 720 to 820 m asl are most exposed to volcanic threats (PDC, lahars, and tephra-fall) along the
268 West margin of the K. Bang, with a few casualties due to overbank PDCs reported in Supit
269 Timur in 2002. Dusun Rowobaung is located on a volcaniclastic fan between two active rivers
270 at 11-12 km from the summit. These dusun host exposed dwellers living in relatively high
271 magnitude and frequency PDC- and lahar-prone zones.

272 3. On the SE flank of Semeru, a group of four dusun belong to Desa Supit Urang. Oro-oro
273 Ombo, away from active valleys, is less exposed than Sumbersari and Gumuk Mas, sitting on
274 a high terrace to the South of the K. Koboan 9 to 11 km from the summit. Curah Koboan is
275 situated 9-10 km from the summit on a high terrace of Curah Lengkong, a tributary to K.

276 Koboan. The population of these *dusun* live in high magnitude / frequency PDC-prone areas,
277 with fatalities in 1994, 1995, 2021, and paid a heavy toll in 2020-2021.

278 4. Kajar Kuning (Desa Sumberwuluh; Kecamatan Candipuro) is located 0.5 km North of
279 Curah Koboan at the foot of hills on which the Volcano Observatory sits at Gunung Sawur.
280 We chose this hamlet to observe how the location away from the Lengkong River would
281 influence the response of less exposed blocks.

282 5. Both *dusun* Jabon and Tulungrejo (Desa and Kecamatan Pasrujambe) are located 10-13 km
283 away on the Semeru East slope on the North side of the K. Tengah valley. The *dusun*
284 Tulungrejo spreads out on the low and middle terraces of the active river, while Jabon is less
285 exposed on ridges 0.8 km north of K. Tengah. We targeted these *dusun* because the Tengah
286 valley was the site of many PDC- and lahar-related fatalities of the 1981 VEI >3 eruption.

287 6. Farther East on the ring plain, three *dusun* Sumbermulyo, Juranglangak and Rekesan (Desa
288 and Kecamatan Senduro) are located in the SE area of the town of Senduro (population c.
289 7,500) 18-19 km from the summit. Senduro, only 20 km away from Kabupaten Lumajang, is
290 also a touristic hub, hosting historic Hindu temples, and a gateway to the Semeru-Tengger
291 massif. These *dusun* host a mixed urban/rural population well away from the volcano and
292 active rivers, hence much less exposed, in a low-frequency fallout-prone zone.

293 **2. Methods**

294 **2.1. Data acquisition and survey procedure**

295 The SE, East and NE slopes of Semeru were the target of two field campaigns in September
296 2018 and 2019. Four researchers from the IPB University, Bogor, the University Gadjah
297 Mada, Yogyakarta (Indonesia), and the Laboratoire Magmas et Volcans, Université Clermont-
298 Auvergne (France) conducted field observations on the population and home exposure to the
299 impacts from PDCs, lahars and tephra fall. A Trimble TC 1000 GIS mapper device, Google
300 Earth maps and topographic maps (scale 1:25,000 as of 2000) have been used in QGIS to
301 locate the surveyed sites and collate structural observations on edifices (homes, offices,
302 schools, health centres, mosques, and markets: Fig. 4; ESD Tables 1, 2). Using a satellite
303 image SPOT5 (pixel: 2.5 m) as of 2014 and Google Earth maps, we outlined the boundaries
304 of *dusun* and built or un-built blocks in the vicinity of principal valleys that convey lahars or
305 PDCs.
The campaigns provided a representative number of observations, as we collected data from 279 households and we mapped 145 blocks in 13 rural *dusun* and 2 small towns. A *dusun* usually entails 4 to 5 *RukunWarga* (RW, a set of 50-75 buildings, mostly homes with a few offices, mosques or schools), which in turn includes 3 to 5 *RukunTetanga* (RT, 20-25 homes).

We collected data from 2 to 4 households per RT, the smallest administrative unit at which respondents can be identified. A *dusun* has an average area of 2.07 km² and density of 806 inhabitants/km², while a built block area is 0.28 km² with a density of 1,397 inhabitants/km² on average (ESD Table 1). We categorized the quality of construction and roof, and building orientation, as these parameters play a role on dwellers' exposure to tephra fall and volcanic flows (ESD Table 2). To complete the 2017 BPS census at the smallest scale possible, we also collected data on population, economic activity (mostly agriculture, husbandry and agroforestry) and emergency facilities from eight governmental offices in: *Desa* (sub-district) Tamansatryan (WSW flank), *Desa* and *Kecamatan* (district) Pronojiwo (South), *Desa* Oro-oro Ombo and Supit Urang (SSE), *Kecamatan* Candipuro and *Desa* Sumberwuluh (SE), *Desa* Pasrujambe, and *Desa* Senduro (East) (Figs. 2, 3; ESD Table 1).

Few limitations encumbered the field survey on multi-component exposure of people and dwellings, which was conducted in Indonesian and/or Javanese thanks to our partners. We interviewed respondents available in the household, either male or female, through a random door-to-door approach and with the interpreter's help. One limitation may be representativeness. Among 950,000 people around Semeru, 50,000 live along active rivers, and up to 100,000 people in areas where eruptive activity exceeds VEI 3. Surveys involving about 279 households and 300 buildings represent approximately 0.6% to 2.6% of the respective cohorts. However, mapping the exposure index involved 145 blocks, hence 6% of the exposed population.

## 2.2. Statistical analyses

Statistical analyses explored a set of 23 variables collected through surveys and organized in three fields: exposure, hazard, and access/response (Fig. 1, Tables 2, 3). The first group of 11 variables and their 43 attributes helped compute the population exposure index of 145 blocks (see 2.2.1 below). The second group of seven variables determined the hazard level of blocks both inside and outside the hazard-prone zones around Semeru (Fig. 3; Thouret et al., 2007).

The third group of five variables defined the access to blocks and response facilities for imminent evacuation.
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Table 1 summarizes the statistical methods and techniques together with their purposes for coding, quantifying and validating three groups of variables and their attributes. Statistical investigation conducted with the R software has involved four operations.

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First, univariate and bivariate analyses were used to extract relevant attributes from variable observations and detect their relationships.

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Second, Polytomous Logistic Regression models helped select optimal exposure variables and predict the exposure index of populated neighbourhoods (EIPN). Logistic regression is a technique commonly applied to highlight dependences between one variable that must be explained (dependent, endogenous, here the Exposure Index of a block) and several variables that explain relationships (independent, exogenous, quantitative and qualitative, ordinal or nominal). Logistic regression allows a model to be developed while the selection between models is done though a set of criteria such as reduction of the Akaike Information Criterion (AIC; Akaike, 1987) and confusion matrix. AIC was designed as the divergence between the true model (that actually generated the data) and a proposed statistic approximation of this model. Polytomous logistical regression (PLR) was performed using Cumulative Links Models to quantify the strength of association between each active variable and the response variable (EIPN score) to be explained.

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Third, multiple correspondence analyses (MCA) of all variables cross-referenced with the EIPN score were used to characterize the blocks. Hierarchical Ascending Classification (HAC) was used to discriminate clusters amongst blocks.

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Fourth, we used the distance/timing criterion combined with the third group of access and response variables to rank blocks with respect to relief operations for evacuation.

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### 2.2.1. Methods to compute the population exposure index in neighbourhoods

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To compute the Exposure Index of Populated Neighbourhoods, we used five techniques (Fig. 1, Table 1):

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1. A normalization technique was used to avoid biases arising from the fact that all qualitative variables do not have the same number of attributes. For each attribute, x being the initial value, we subtract the minimum value and we divide by the maximum interval, as follows:

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$$x_{normalized} = \frac{x-x_{min}}{x_{max}-x_{min}} \in [0,1]$$
Thus, all values of qualitative variables are equidistributed between 0 and 1.

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2. A Chi² test was performed to determine whether the variables are dependent or independent (ESD Table 3). In other words, to find out whether the difference between observed and expected data is random or due to a relationship between the variables under study.

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3. The non-parametric Wilcoxon test (Wilcoxon, 1945) was used because some pairs of variables were identified as independent according to the Chi² test. This test allowed us to test whether two variables show similar distributions.

Based on results of the Chi² and Wilcoxon tests, we determined that all variables were dependent and followed similar distributions. The sum of the normalized values of 11 variables and their 43 attributes for each of the 145 blocks provided the EIPN score.

4. The Shapiro-Wilks test (Shapiro and Wilks, 1965) can be used to determine whether a particular dataset follows a normal distribution, which is a common assumption used in many statistical tests. The Shapiro-Wilks test is based on the null hypothesis according to which the EIPN score follows a normal distribution. We discarded the null hypothesis when the p-value was lower that the α risk fixed to 0.5 (5%).

5. Discretization was based on Jenks optimization method or natural breaks classification method (Jenks, 1967) because the distribution of values does not follow a normal distribution and the number of attributes varies between variables. The EIPN score parametric distance between classes is calculated using the Jenks technique. As it minimizes the intraclass variance while maximizing the interclass variance, we fix the number of breaks, i.e., number of discretized classes. The Jenks-type discretization, as opposed to other methods, allowed us to fit data to the shape of the statistical distribution, while it provided four homogenous classes.

### 2.2.2. Statistical analyses to characterize block exposure, hazards, and access/response

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4. The Shapiro-Wilks test (Shapiro and Wilks, 1965) can be used to determine whether a particular dataset follows a normal distribution, which is a common assumption used in many statistical tests. The Shapiro-Wilks test is based on the null hypothesis according to which the EIPN score follows a normal distribution. We discarded the null hypothesis when the p-value was lower that the α risk fixed to 0.5 (5%).

5. Discretization was based on Jenks optimization method or natural breaks classification method (Jenks, 1967) because the distribution of values does not follow a normal distribution and the number of attributes varies between variables. The EIPN score parametric distance between classes is calculated using the Jenks technique. As it minimizes the intraclass variance while maximizing the interclass variance, we fix the number of breaks, i.e., number of discretized classes. The Jenks-type discretization, as opposed to other methods, allowed us to fit data to the shape of the statistical distribution, while it provided four homogenous classes.

### 2.2.2. Statistical analyses to characterize block exposure, hazards, and access/response

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6. Bivariate analysis (BA, Table 1) involves the analysis of two variables with the aim to test simple hypotheses of association or any relationship between two variables and attributes. The Chi-square test aims to compare observed results with the expected results (see the test purpose in section 2.2.1 above; ESD Table 3). Factorial Correspondence Analysis (FCA) is

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1. Univariate analysis (UA, Table 1) is the first step for exploring and preparing a dataset for further analysis. UA summarizes descriptive statistics and provides graphical representations for their univariate distribution (e.g., Chambers et al., 2018).

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2. Bivariate analysis (BA, Table 1) involves the analysis of two variables with the aim to test simple hypotheses of association or any relationship between two variables and attributes. The Chi-square test aims to compare observed results with the expected results (see the test purpose in section 2.2.1 above; ESD Table 3). Factorial Correspondence Analysis (FCA) is

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used to study links between two qualitative variables and explore correlations and oppositions
of categorical variables in a table of frequencies or contingency (ESD Table 4). From these
correlations and oppositions, FCA thus allows us to state hypotheses to identify typical blocks
in the Semeru dusun.

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3. Multiple Correspondence Analysis (MCA) is run when a set of observations includes multiple qualitative variables. MCA is a data analysis technique used to detect and represent underlying structures in a large categorical dataset (Abdi and Valentin, 2007). This technique represents data as points, illustrated by biplot graphs. MCA consists of the following steps (Table 1; ESD Fig. 3):

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(1) Eigenvalues helped define the number of retained dimensions having the highest inertia rate, hence the most relevant information from 81 attributes of 23 variables. Benzécri correction (Benzécri, 1979) applied to the number of dimensions (inertia) helps estimate how much information is included in each dimension and select the smallest number of dimensions which contain the maximum information available (ESD Fig. 2). We retained the first three dimensions, which contained almost 91% of the information.

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(2) Biplots show the relationships between attributes of all variables, while taking into account the quality of representation quantified by square cosines, contribution and coordinates of each attribute.

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(3) Scatter plots establish relationships between the attributes among the same qualitative variable. Confidence ellipses highlight proximities (closeness) and oppositions between the attributes of one variable in the projection of blocks along a given axis in scatterplots.

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MCA procedure includes two tests: Fisher test to highlight the correlation between a given variable and the factorial axis and Student test to verify which attributes have singular coordinates along the axis (dimension). ESD Table 5 displays the coordinates, squared cosines and contributions of the attributes with respect to dimensions. These contributions help select the contributory variables retaining more information and their attributes. Squared cosines show the quality of representation and help select variables with sufficient quality within the most contributory ones. Coordinates of this table indicate the position of attributes, the most distant from the barycentre of each biplot contributing most.

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4. Hierarchical Agglomerative Clustering (HAC; see Table 1) was performed on the MCA outputs to confirm previous MCA groups and/or construct groups of blocks sharing similar statistical characteristics. HAC leads to a factor map identifying four groups of blocks (ESD Fig. 4) based on results from frequencies of attributes.

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432 **3. Statistical results**

433 The statistical analysis using UA, BA and MCA techniques of 11 exposure variables and their
434 attributes (Table 5, ESD Table 3) aimed to predict the variations in the index of population
435 exposure between blocks.

436 **3.1. Defining variables for the multi-component exposure index**

437 To compute the EIPN at the block scale, we used 11 variables and their attributes: 9 variables
438 increase the exposure to volcanic threats, to which we added two defining the exposure of
439 dwellings (Fig. 1; Table 2; ESD Table 3). Each variable shows attributes divided by
440 thresholds, amounting to 43 attributes (Table 2). A number from 0 to 5 is attributed to each
441 attribute, although variable V8 exceptionally has 10 attributes (Table 2). One variable V4 only
442 was attributed a weighting coefficient (see the method involving four steps: Table 3). The
443 rationale for each variable and its attributes relies on risk-related exposure works (e.g., Ewert,
444 2007; Ewert et al., 2018; Loughlin et al., 2015; Nieto-Torres et al., 2021), as follows:

445 **V1:** Three circle radii around the vent (Fig. 2) are: a maximum 9 km distance at which
446 ballistics and heavy (lapilli-sized) tephra-fall occur and may impact roofs commonly made of
447 tiles supported by light timber; a maximum 12 km distance at which the longest PDCs
448 reached settlements in recent history (e.g., in 2002 with VEI 3), and an average 18 km
449 distance for lahars, exceptionally 35 km to the sea for the most voluminous ones (e.g., in
450 2021). The energy line concept (Malin and Sheridan, 1982) yields maximum runout distances
451 between 12 and 18 km for Semeru's PDCs while the 30-35 km range is commonly reported
452 for voluminous lahars (e.g., Ewert et al., 2005). Each of the circle radii were assigned the
453 following values based on the distribution of *dusun*: 9-12 km= 3, as 71% of *dusun* are located
454 and most of prior fatalities occurred within this range; 12 to 18 km= 2 (22% of *dusun*); and <
455 9 km= 1 (7% of *dusun*).

456 **V2:** We retrieved population density at the *dusun* scale from the 2017 census (BPS, 2017).

457 **V3:** Population density within the built blocks was estimated from house counting (4 persons
458 on average per household) on Google maps and field observations with an error range of c.
459 10%. We ranked densities in increasing order from the lowest to the highest threshold (using
460 quartile interval) to reflect the increasing effect on people exposure in both *dusun* and blocks
461 (Table 2).
V4: Table 3 shows the four steps of the methods used to compute the coefficients for V4. The inferred number of fatalities from the recorded eruption dataset (Table 2 in Thouret et al., 2007), total 1,115 victims from 18 events reported since 1884. As about 950,000 people live in the 35 km-distance circle with an area of ca. 1380 km² around the volcano, the ratio yields c. 1175 victims per million people, c. 8.5 victims per year, and c. 62 fatalities per event on average. These figures are likely under-estimated, while numbers of injured or displaced people are possibly large.

Using the **hazard zones affected by the historical eruptive activity and lahars** (Fig. 3) and the number and approximate location of fatalities, we chose the circle distances at 9, 12, 18 and 35 km. Population numbers in villages per circle area are 2,864 with radius distances of 9 km, 59,236 with 12 km; 85,303 with 18 km, and 842,597 people with 35 km radius, based on our calculation of the number of houses in the circles 1 to 3, and on the BPS 2017 census beyond 18 km. The weighted population (fatalities and distances) according to Brown et al.'s (2015a) method (Table 3) is 103 in circle of 9 km radius; 4,858 in circle of 12 km; 15,739 in circle of 18 km; and 587,800 in circle of 35 km radius. As 1,115 fatalities are reported for a population of 26,432 living in the 15 surveyed *dusun* based on 9 km, 12 and 18 km circle distances (Fig. 2), the final weighting coefficient for V4 is 0.036 for the 9-km circle, 0.082 for the 12-km circle, 0.185 for the 18-km circle, and 0.698 for the 35-km circle (Table 3).

V5: Location of fatalities with respect to valleys that transmit flows across three sites with values ranked from 3 to 0 according to hazard-type effects: 3 is given to valley margins reached by pyroclastic surges and overbank lahar flows; 2 is assigned to valley channels conveying confined pyroclastic flows and lahars, and terraces or interfluves swept by ash-cloud surges up to 500 m from the river; 0 indicates ‘no reported death’ along valleys (Table 2).

V6: Lahar fatalities is a binary variable (Yes 1/No 0) and both figures 1,0 have been distributed along the valley channels, terraces and margins.

V7: PDC fatalities is also binary (Yes 1/No 0), and spatially distributed, valley-confined flows being distinct from un-confined surges on valley margins and adjacent interfluves.

V8: Location of mapped blocks on terraces is based on a range of distances from, and elevations above the river channel. Ten cases based on both criteria involved distance thresholds from 15-50 m to > 120 m, and elevation thresholds from < 2 m to > 30 m. We attributed marks 10 to 1 based on decreasing exposure to lahars and PDCs, from the maximum 10 for channels and the minimum 1 for high and distant terraces.
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**V9:** Timely location of people living and/or working on Semeru's flanks encompass three situations in decreasing exposure duration order: permanent, daily work and home at night; temporary, home at night only, and; temporary, daily work only and night elsewhere.

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To the 9 variables that define human exposure we added two variables that usually are criteria defining the physical vulnerability of buildings. Here we consider them within the exposure field, as they determine the extent to which residents are exposed to volcanic threats owing to tephra fallout load on roofs and pressure impacts from PDCs and lahars on homes.

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**V10:** Orientation of house, i.e., perpendicular, oblique or parallel to PDC and lahars.

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**V11:** The quality and type of roof and construction are ranked as poor (wood), regular (zinc) with light timber, and fair when the roof is concrete and/or covered by tiles supported by sturdy timber. Almost all roofs, being pitched and many being covered by tiles or zinc, could probably withstand about 20 cm-thick tephra-fall, but concomitant, frequent rainfall would diminish the resistance threshold, in particular for roofs of light timber.

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## 3.2. Computing the exposure index of populated neighbourhoods (EIPN)

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The table in Figure 5 and ESD Table 6 display the EIPN score (from 1.17 to 8.56, median 4.17) obtained for each block after discretization. ESD Table 6 shows columns as variables and their thresholded attributes, while rows are the 145 surveyed *dusun* blocks. As a result, four colour-coded EIPN scores levels, from ‘residual’ (beige) to ‘very high’ (red), have been attributed to each of the 145 blocks (Fig. 5 A-F). The relatively narrow range of EIPN (standard deviation c. 1.05) reflects a relatively low exposure of all *dusun* to the potential effects of the Semeru activity except those which are located inside the proximal hazard zones and along valley channels depicted in Figure 3. The EIPN score displays three groups of blocks (Figs. 4): c. 39% of blocks show a moderate to high EIPN, and almost 25%, located in the vicinity of valley channels, show a very high EIPN. On the other end, almost 37% of the blocks show a very low or residual EIPN score, which correspond to older neighbourhoods that used to settle away from the active valleys. At this stage, we need to explore the relationships between the attributes of exposure variables to highlight the most contributory ones.

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### 3.2.1. Relationships between attributes of exposure variables

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MCA scatter plots represent attributes using elliptic envelopes of confidence around the barycentre of groups (Fig. 6). Ellipses of 36 attributes that belong to the 11 exposure variables
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were plotted in the dimensions 1 and 2 scatter plots (Fig. 6). A number of opposed attributes (i.e. away from the barycentre) lead to the following inferences:

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1) The 9-12 km circle distance stands out among other distances from the vent, coinciding with the majority of exposed *dusun* within this circle. 2) In V2 and V3 scatter plots as well as in the V9 plot (timely location), the ‘0 density’ stands out, which identifies temporary people location and un-built blocks. 3) The attributes valley channel and valley margins, although they are less distinct from other ‘fatalities locations’, coincide with river channels and low-altitude terraces that are more exposed to lahars and PDCs. 4) In V6 and V7 scatter plots, the presence/absence of prior fatalities due to PDCs and lahars also suggest the extent to which blocks are exposed. 5) The V8 plot shows that the relationship distance/elevation to the channel is not discriminant enough, except for the river channel. 6) The V10 and V11 scatter plots also suggest that building parameters (quality of roof, construction, and house orientation) may be significant to dwellers’ exposure.

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As a result, the following eight contributory attributes of nine exposure variables may characterize the block exposure: the 9-12 km circle distance (V1), the *dusun* and block density (V2, V3) and temporary location (V9), the channel and low terrace location for prior fatalities (V8), the presence of PDCs and lahars (V6, V7), and both building parameters (V10, V11).
For the sake of research efficiency, we reduced the number of variables to be collected in the field for predicting the EIPN.

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**3.3. Predictive capacity of Polytomous Logistic Regression to obtain EIPN scores**

Polytomous Logistic Regression (PLR) is one of the logistic regressions adapted to the study of categorical and/or ordinal variables with more than two attributes (Kleinbaum and Klein, 2010). As the EIPN score, variable of interest, is qualitative, PLR is the most adequate type of regression, involving a succession of techniques (Table 1):

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**3.3.1. Selection of a small number of active variables, and model significativity**

The initial PLR model included 11 variables and their 43 attributes (Table 4A). In fact, the selection procedure reduced 11 variables down to 8 to avoid collinearity (a linear relationship between two explanatory variables); for example, V1 distance to vent and V4 ‘Brown’s PEI’ parameter are dependent and collinear (i.e., retaining the same information and same distribution) with V2 and V3 (population density), hence V4 was not retained (Table 4A). We performed backward and forward model selection procedures, which consisted in sequentially adding or removing variables and investigating the resulting model performances. During backward or forward selection processes, AIC offers a compromise between parsimony and
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error reduction by penalizing a high number of parameters. The model having the smallest AIC possible was chosen to select optimal variables.

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Table 4A shows the initial PLR model with estimated coefficients of the selected attributes. ‘Significativity’, indicating that the underlying model coefficients are significantly different from 0, is the most important criterion for prediction. Based on the significativity, ‘3 stars’ are attributed to attributes with < 0.1% error in the model, ‘2 stars’ to attributes with < 1% error and ‘1 star’ to those with < 5% error (Table 4B). According to the significativity results, a rigorous model would only include eleven 3-stars attributes with high probability. The selected PLR model (Table 4B) then reduced the non-collinear 8 variables to 6 only: V1 distance to vent; V2 density of *dusun*, V3 density of blocks, V5 Location of prior fatalities, V6 Lahar fatalities, and V7 PDC fatalities. The model also reduces the 43 attributes down to 15, as shown in Table 4B.

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The selected model comprises both positive and negative coefficients (Table 4B). Positive coefficients mean that when attributes increase, the EIPN score increases as well, while negative coefficients diminish the EIPN score. Thus, the attributes of the following variables: V1 distance to vent; V2 density of *dusun*, V3 density of blocks and V5 Location of prior fatalities will increase the chance that the blocks score a high EIPN, i.e., are more exposed. Conversely, blocks that possess low thresholds of both variables, V6 Lahar fatalities, and V7 PDC fatalities, will have a weak EIPN score: both hazards threatening these blocks are frequent, but they do not imply a large number of fatalities.

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### 3.3.2. Probability of obtaining a block exposure index and quality of the model

PLR allows us to compute the probability of getting EIPN for any given block. We first assessed the quality of the PLR model (Table 4): the calculated MSE is 0.179, while the efficient ranking rate is as high as 82.07%. We used a Table termed confusion matrix (ESD Table 7), which crosses ‘real’ observed EIPN scores with predicted ones, to test the quality of the estimated model. For a ‘perfect’ model, the confusion matrix would show a diagonal matrix in which non-diagonal entries are all equal to zero. The model is efficient if the number of non-diagonal, predicted values is below 40% of the total number of observed values (Kleinbaum and Klein, 2010). Among 145 observed blocks, we cannot predict 26, yielding a relatively low error rate of c. 18%, which ensures relative efficiency to the model.

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Two blocks located in the town of Pronojiwo and in the *dusun* Rowobaung (Fig. 5A) are given in the table below as examples based on specific attributes of six variables: R3, away from both
active rivers (K. Kembar, K. Bang), has been attributed a residual EIPN score, whereas P1, located near the K. Bang valley channel, has been attributed a very high EIPN score in maps of exposed blocks (Fig. 5A). We compute the EIPN probability of both blocks in order to test how well PLR predicts the score:

<table><thead><tr><th>Block no.P1<br>Pronojiwo<br>Fig. 5A</th><th>V1<br>distance<br>to vent</th><th>V2 density<br>of <i>dusun</i></th><th>V3 density<br>of block</th><th>V5<br>location</th><th>V6 Lahar<br>fatalities</th><th>V7 PDC<br>fatalities</th></tr></thead><tbody><tr><td>P1</td><td>9 - 12 km</td><td>1,091-1,690</td><td>891-1,340</td><td>Valley channel</td><td>No</td><td>Yes</td></tr><tr><td>R3</td><td>9 - 12 km</td><td>349 - 823</td><td></td><td>Un-reported<br>fatality</td><td>No</td><td>No</td></tr></tbody></table>

Using the function 'predict', the probabilistic results for P1 block are as follows:

EIPN score prediction for block P1

<table><thead><tr><th>Very high</th><th>Moderate - High</th><th>Very Low - Low</th><th>Residual</th></tr></thead><tbody><tr><td>0.9964144</td><td>0.003583707</td><td>1.850412e-06</td><td>2.701923e-09</td></tr></tbody></table>

Thus, the P1 block has 99.64% probability of obtaining a very high EIPN score. The result shows that the selected model is working well, as we calculated a very high EIPN score for P1 (Fig. 5A). Using the same function, the probabilistic results for the R3 block yields:

EIPN score prediction for block R3

<table><thead><tr><th>Very high</th><th>Moderate - High</th><th>Very Low - Low</th><th>Residual</th></tr></thead><tbody><tr><td>1.602824e-07</td><td>0.0003109862</td><td>0.1756036</td><td>0.8240853</td></tr></tbody></table>

Thus, the R3 block has 82.41% probability of obtaining a residual EIPN score. The result shows again that the selected model is valid, as we calculated a residual EIPN score for R3 (Fig. 5A).

4. Mapping the composite exposure index at the scale of *dusun* neighbourhoods

Maps display levels of the colour-coded EIPN score of 145 blocks (Fig. 5 A-F, ESD Table 6). The most exposed, active valleys convey lahars and PDCs, in particular the large valley channels (K. Koboan to the SE, K. Bang and Kembar to the S, and K. Tengah to the E). These wide channels host no permanent homes, but only temporary shelters and small makeshift shops, located on low terraces close to the river. Workers extracting material are highly exposed to lahar and PDC impacts.

1. Most exposed *dusun* blocks to the current eruptive activity of Semeru are located along the principal valleys that convey lahars and/or that have been affected by PDCs over the past 140
613 years. These are Supit Timur near K. Bang, North and West blocks of Rowobaung between K.
614 Bang and K. Kembar (Fig. 5A), affected in 2002. Highly exposed are the North blocks of
615 Sumbersari and Curah Koboan on both margins of K. Koboan (heavily damaged in December
616 2020 and January 2021), and on the North bank of Curah Lengkong (Fig. 5B). The blocks of
617 Tulungrejo (Pasrujambe) on the low and middle banks of K. Tengah are also highly exposed
618 (Fig. 5C), while dykes built in the years 1970-1980 along the North bank of K. Tengah may
619 provide a false sense of security.

620 2. Increasing eruptive activity producing the VEI 3 events every 8 to 11 years. At such times
621 additional blocks would suffer heavy tephra-fall, lahars, and ash-cloud surges from PDCs
622 along river valleys. Such *dusun* blocks encompass Supit along K. Bang, the East blocks of
623 Pronojiwo near the bridge across K. Bang, most of Rowobaung between K. Bang and K.
624 Kembar (Fig. 5A), most of Sumbersari and part of Gumuk Mas and Curah Koboan near K.
625 Koboan (Fig. 5B), and most of Tulungrejo in the Desa Pasrujambe (Fig. 5C).

626 3. Other *dusun* blocks located down valley on low terraces can be affected by large lahars,
627 e.g., K. Koboan down valley as far as Sumberwuluh (ESD Fig. 1D), as happened in January
628 2021, or by ash-cloud surges as far as Tawonsongo upstream of K. Tengah (Fig. 5C). Heavy
629 tephra-fall can affect the *dusun* of Blubuk and Karangusko in large VEI ≥ 3 eruptions (Fig.
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631 4. Blocks with very low to residual EIPN characterize the town of Senduro 17-19 km away
632 from Semeru vent and away from active rivers (Fig. 5E). Senduro's dwellers are the least
633 exposed, except for tephra-fall dispersed from Semeru in large (VEI ≥ 4) but un-frequent
634 eruptions, and from Bromo (Tengger caldera) in 2009-2011, and 2020.

635 5. Contrasting EIPN scores of blocks situated in the same *dusun* are due to spatial changes
636 and specific attributes at a local scale, such as density, hazard frequency, home quality and
637 orientation, and dirt roads instead of paved roads (e.g., Supit and Pronojiwo, Fig. 5A).

638 Decreasing EIPN to moderate level across *dusun* is due to decreasing densities and increasing
639 elevation and/or distance from the channel (e.g., North and East blocks of *dusun* Curah
640 Koboan and Sumbersari down valley, Fig. 5B, upper area of Tulungrejo and Tawonsongo,
641 Fig. 5C). Different EIPN scores are also due to the distance to health centres and locations
642 with limited lahar-related fatalities (e.g., Blubuk and Karangsuko, Fig. 5D).

643 EIPN maps reflect the 2018-2019 situation when Semeru eruptive activity was mild (VEI 2)
644 and the alert status was 2 on a scale of 4. As Semeru activity escalated to VEI 3 in December
645 2020 and January 2021, triggering dome-collapse PDCs and tephra-fall from > 6 km-high
646
647

columns, together with hot lahars (GVP, 2022), every EIPN score would increase to the next
higher colour-coded level (see EIPN Table in Fig. 5A-C).

648

**5. Characterizing blocks (hazard, access/response) based on statistical analyses**

649
650
651

We used UA, BA and multivariate analyses to define the characteristics of blocks based on hazard and access/response variables, compared with the EIPN score. We defined the variables of the second and third groups as follows:

652

**5.1. Second group of variables: volcanic hazards**

653
654
655

The second group of variables (Table 2) determines the hazard level of neighbourhoods inside and outside the hazard-prone zones outlined around Semeru (Fig. 3; see Thouret et al., 2007 for the type, occurrence and frequency of lahars, PDCs, and tephra fallout.)

656
657
658

**V12:** Volcanic Explosive Index is ranked according to current, daily (since 1967) and chronic, episodes of increased eruptive activity: VEI 1-2 unrest and mild activity, VEI 3 every 8 to 11 years on average, and VEI ≥ 4 every 25 years on average.

659
660
661

**V13:** Two types of lahars are debris flows (DF) and hyperconcentrated flows (HCF), ranked in three attributes: DF and/or HCF, HCF alone, and absent. Frequent lahars occur every rain season, and shortly after eruption.

662
663

**V14:** Lahar frequency is in decreasing interval order from high, week or months, to low: > 25 years.

664
665
666
667

**V15:** Two PDC types are valley-confined pyroclastic flows affecting valley channels and banks, and un-confined, pyroclastic surges affecting valley terraces and sometimes adjacent interfluves. Because valley margins are populated, we ranked surge first, then confined PFs, and last, interfluves exceptionally affected > 0.5 km from valley channels.

668
669

**V16:** The PDC frequency range follows the same time interval order from high (1-8 years) to low > 25 years.

670
671

**V17:** Tephra fallout types are twofold: ballistics and lapilli within 8-9 km from the vent and 1 for tephra-fall beyond the 8-9 km distance.

672

**V18:** Tephra-fall frequency is ranked in decreasing interval order from < 1 year to 5-25 years.

673

**5.2. Third group of variables: access and response**

674
675
676
677

The third group defines the access to blocks and the response to imminent eruption including evacuation (Table 2), ranked on distance and quality criteria. We attributed the maximum mark to the poorest quality or to the absent attribute of five variables, as the ultimate goal was to rank a range of blocks based on exposure and remoteness in case of crisis.
678
679

**V19:** Three types of access ways according to the decreasing quality of the network, from paved road, dirt road to trail.

680
681

**V20:** Evacuation roads, shelters and storage facilities include distance (5 km-threshold, i.e., 20 minutes driving a 4x4 vehicle or c. 1 hour walking) and absence from the *dusun*.

682

**V21:** Civil Protection works, such as dykes and check dams, are present or absent.

683
684
685

**V22:** Early warning system and local offices of the Indonesian board for natural disaster management (*Badan Nasional Penanggulangan Bencana BNPB*) are ranked according to distance (2.5 km-threshold, i.e., within walking distance) or absence.

686
687
688

**V23:** Health centres: four hospitals around Semeru are located in Dampit, Pronojiwo, Pasirian and Lumajang, but at least one small hospital or dispensary is present in all *Desa*. Distance (10 km- and 5 km-threshold) and presence or absence helped rank the health centres.

689

**5.3. Attributes of representative blocks based on univariate analysis**

690
691

Univariate analysis (UA, Fig. 7) was used to compute the frequency of attributes of all variables from the dataset including 145 *dusun* blocks.

692

First, the most frequent attributes allow us to elaborate on representative blocks (Fig. 7):

693
694
695
696
697
698
699
700
701

(1) As many as 84% of blocks are built and permanently occupied, 75% are located between 9 and 12 km from the summit, and most of them are located on high (> 10 m) terraces at mid-distance (> 120 m) from the valley channel. (2) About 62% have recorded 0 fatalities, but most of them can be affected by light ashfall within 1 to 3 years interval; almost 27% of blocks can be damaged by lahars in valleys nearby, and 10% to 19% recorded PDC- and lahar-related fatalities, respectively. (3) The construction quality of 75% of homes is regular.

(4) About 61% of blocks are close to a shelter or storage facility and c. 77% of blocks lie within 10 km of hospitals. However, this means that c. 23% of blocks lie ≥ 10 km from hospitals, while 51% remain poorly connected to other response facilities.

702
703
704
705
706
707

Second, UA results highlight the most relevant variable attributes among the blocks (Fig. 7):

(a) the population density of *dusun* and built blocks is discriminant, but higher densities of built blocks increase human exposure to volcanic threats; (b) the location of c. 24% of blocks on low to middle terraces and within short to middle distance to river channel may be more discriminant than the location of prior fatalities (as c. 62% of blocks record no death) and timely location of people (as homes in 83.50% of blocks are permanent).
Third, strong contrasts in exposure of inhabitants are due to the block location with respect to rivers due to lahar and PDC type, frequency, extent, and impacts. Blocks without reported fatalities prevail (c. 62%) in *dusun*, which long settled away from the active valley channels.

Among PDCs, the predominant, un-confined surges reflect the correlation between fatalities and valley terraces on which few *dusun* have recently spread out. The impact of the daily tephra-fall hazard on blocks is binary: very few *dusun* lie within the 8-9 km radius from the vent affected by ballistics and lapilli from the recurrent, low-altitude columns at the summit, whereas decreasing tephra-fall frequency beyond 9 km has little impacts on *dusun* blocks if VEI remains low (< 3).

Fourth, hazard impacts depend on perpendicular or oblique orientation of buildings, which renders almost 63% of homes exposed to flows, while the poor (5.5%) and regular (c. 76%) quality of roofs would not withstand thick (≥ 25 cm) and wet tephra-fall deposit (Fig. 4).

About 51% of the blocks show poor access ways and most of them (c. 85%) lack civil protection works (dykes, dams) against volcanic flows, while check dams across active valleys remain filled up or damaged. In contrast, three variables of the third group may counterbalance eruption impacts, therefore reducing block exposure (Fig. 7): (a) Almost half of the ways are paved roads, favouring access to *dusun*; (b) evacuation shelters, roads signs and storage facilities do exist in c. 61% of blocks, and; (c) early warning system and BNPB offices are close enough (< 2.5 km) to c. 81% of blocks, while c. 77% of *dusun* lie within 10 km of any hospital.

## 5.4. Block definition based on BA and MCA of attributes

MCA was used to find statistical relationships between the exposure index score and other attributes that belong to hazards, access and response.

First, contributory variables of MCA were tested by means of Chi-square test (Table 5) to find out how dependent they are with other contributory variables to whom they are correlated.

The $\text{Chi}^2$ test shows a strong statistical link (95% confidence) between variables indicated as dependent in Table 5 (bold figures) and ESD Table 3. There are six exposure variables: V2 density of *dusun* and V3 density of built block population, V8 Terrace elevation/distance relationship, V9 Timely location of people, V10 House orientation, and V11 Roof and construction quality. Table 5 also include variables outside exposure that are dependent with the majority of variables: V13 Lahars, V15 PDCs, V16 PDC frequency, V18 tephra-fall frequency, V19 access roads, and V22 Early warning system and BNPB office.
740 Second, colour-coded MCA biplots show how the attributes contribute to the definition of
741 blocks according to the Dimensions 1 and 2 (Fig. 8A) and Dimensions 2 and 3 (Fig. 8B).

742 These graphs display proximities and oppositions between all attributes: the darker the colour,
743 the more contributory the attribute is; conversely, light colour points to less contribution.

744 MCA biplots (Figs. 8A, B) distinguish four groups of attributes based on contributions and
745 squared cosines (ESD Table 6).

746 **5.4.1. Four groups of attributes in the Dimensions 1 & 2 and 2 & 3 biplots**

747 1. The right-hand side group along Dimension 1 include the following attributes: channel and
748 low terrace/short distance (V8), both attributes DF or HCF (V13), 0 inhabitant density (V2,
749 V3) together with the ‘temporary location’ attribute of V9. Such attributes point to un-built
750 blocks located along the valley channels where shelters are temporary and people used to
751 work daily and occasionally. These un-built blocks or temporary settlements, being not
752 constantly exposed, show a residual to moderate EIPN. They recorded lahars and PDCs
753 fatalities as they can be hit by both confined pyroclastic flows and unconfined surges, while
754 their access by trail is challenging.

755 2. Two groups are positively and negatively correlated along Dimension 2 (Fig. 8A). The
756 second group of attributes, positively correlated, define blocks with residual EIPN due to long
757 distance and high elevation from the rivers (V8), absent lahars and PDCs (V13, V15),
758 together with low frequency of tephra fall (V18). The relatively densely populated blocks are
759 located within 12 and 20 km from vent, but they are devoid of evacuation shelter, early
760 warning system, and civil protection office.

761 The third group is observed below the graph barycentre (Fig. 8A), but the quality of
762 contribution is weaker than that of the previous group. Several attributes include: 9 to 12 km
763 (V1), high block density (V3), PDC surge (V15) every 8 to 25 years (V16), ashfall every 1 to
764 3 years (V18), and existing civil protection works (V21). These blocks, located in the most
765 affected 9-12 km circle distance from the volcano summit, may be damaged by tephra fall and
766 sometimes by lahars and PDCs, which characterize their moderate to very high EIPN.

767 Figure 8B shows the contribution of variable attributes in the Dimensions 2 and 3 biplot,
768 although the contribution is relatively weak. The small fourth group of correlated,
769 contributory attributes appears in the upper right corner of the biplot: distance < 9 km (V1),
770 Lapilli and ash within 9 km from vent (V15), < 1 year (V16), and no health centre (V23).
Such attributes point to blocks located near the volcano summit (> 1,000 masl) liable to be affected by tephra fall and far from available emergency facilities.

773

## 6. Statistical grouping of blocks; Application to relief operation in case of evacuation

### 6.1. Four groups of blocks result from MCA and HAC

Attributes extracted from MCA (Fig. 8) and HAC (Table 6, ESD Fig. 4) point to four groups of blocks, ranked according to the most discriminant variables (see the Chi² test conducted on HAC variables, ESD Table 8) and the EIPN score. These groups are similar to those defined using MCA biplots.

Group 1 looks like the first group described in subsection 5.4.1: un-built blocks located along active valley channels and their margins are temporarily occupied by laborers or workers. The location explains why they are affected by all types of and frequent lahars and PDCs, inducing fatalities or injuries. Trails and dirt roads hinder access to these blocks, which remain blocked from any health centre. Examples are located on the margins of K. Bang and Kembar (Dusun Supit Timor), K. Koboan (Dusun Sumbersari), and K. Tengah (Dusun Tulungrejo) (Fig. 5A-C).

Group 2 is similar to the second group (see 5.4.1). Built blocks with residual EIPN are located farther away (12 to 20 km) from the volcano, on low slopes and in the ring plain. These blocks are occasionally affected by distal lahars along the active valleys and light, relatively un-frequent tephra-fall associated with long-reach PDCs from VEI≥ 3 eruptions. Most of them are located away from the valley channels, but low-frequency surges may reach them during large, un-frequent eruptions. What makes the exposure residual is the average density of both blocks and *dusun*, and the lack of response facilities. Examples are Kajar Kuning, Gumuk Mas, Oro-Oro Ombo, and Jabon (Fig. 5B, and C).

Group 3 looks like the third group (see 5.4.1). Densely populated built blocks within highly populated *dusun*, located between 9 and 12 km distance, have long settled on high terraces away from the valley channels. Un-affected by lahars and valley-confined PDCs, these blocks can be impacted by relatively frequent tephra-fall and low-frequency surges. Such blocks exhibit moderate EIPN, when response facilities are close and they are protected by civil protection works (e.g., Oro-Oro Ombo and West of Sumbersari, Fig. 5B). Other blocks
801

exhibit high EIPN if they remain un-protected and remote from response facilities (Supit Timor, Rowobaung, Fig. 5A; Curah Koboan, Fig. 5B).

802

803

Group 4 resembles the fourth group (see 5.4.1). Blocks within 9 km from Semeru's vent, frequently affected by light tephra-fall, are located higher up on the volcano's flanks, far from the main roads, response facilities such as hospitals. These blocks with poor house construction quality and small dispensaries exhibit very low or low EIPN, such as Blubuk and Karangsuko (Fig. 5D), because the people density is low and they are affected by light ashfall only.

804

805

806

807

808

809

## 6.2. Application to relief operations based on access to blocks and response facilities

810

Our method to characterize blocks around Semeru was applied to rank them to highlight potential challenges in evacuation procedure. First, the distance/time criterion is computed to evaluate evacuation based on access to the blocks and means of transport. This criterion was related to distances to the shelters and/or storage facilities and health centres, as shown by Table 7 and ESD Table 9, ESD Figure 5. Second, we used the results of analysis of attribute frequencies (Table 7) to distinguish a range of blocks based on access to response facilities. Factor map (ESD Figure 5) helps distinguish the number of clusters. Maps in Figure 9 display a range of four block clusters based on access versus remoteness in case of evacuation. Blocks accessible only by dirt roads (e.g., Sumbersari, Curah Koboan along K. Koboan, Fig. 9A; Tulungrejo along K. Tenggah, Fig. 9B; Supit and Supit Timor along K. Bang, Fig. 9C) are challenging for relief operations in case of evacuation. This was reflected by the cumbersome evacuation of injured and affected people following the 4 December 2021 eruption on both mid reaches of the Koboan valley. Dusun like Rowobaung (Fig. 9C) linked to Pronojiwo by one bridge would remain cut off by lahars propagating along two rivers surrounding the blocks. Dusun located high on the Semeru' slopes with narrow dirt roads (e.g., Blubuk and Karangsuko, Fig. 9D) include upstream blocks at high elevation (> 1,000 m asl) connected only by trails that may become blocked by heavy tephra fall. The town of Senduro, far from active rivers and well connected by paved roads to Lumajang, can be evacuated, but the narrow street network in densely populated dusun may be challenging (Fig. 9E).

811

812

813

814

815

816

817

818

819

820

821

822

823

824

825

826

827

828

829

## 7. DISCUSSION
Table 8 compares the number of exposure parameters from the literature with our local-scale, multi-component EIPN method. Specific characteristics of exposure arise from this comparison.

Globally, two criteria measure human exposure index using the distribution of population potentially affected by a volcanic eruption: either the (log) number or the density per area (e.g., Wild et al., 2021; Nieto-Torres et al., 2021). The majority of publications, following Yokoyama et al. (1984) and Ewert (2007), consider the number or density of population within circle distance or radii from the main crater. Recent studies correlated the circle-distance thresholds with fatalities (Auker et al., 2013; Brown et al., 2017) and population density (Freire et al., 2019). A limited number of studies encompass several parameters of human exposure (e.g., five among 10 parameters describing exposure on the Nisyros Island; Kinvig et al., 2016). The most complete study to date in ranking volcanic risk (Nieto-Torres et al., 2021) includes nine exposure parameters within a total of 41 covering hazard, exposure, vulnerability and resilience. However, their single human exposure parameter is the density of population within 5, 10, 30 and 100 km radii.

In contrast, the multi-component EIPN is based on six optimal variables using PLR models from a set of 11 initial exposure variables. Instead of a simple statistical technique to weigh and sum exposure parameters, statistical operations enabled us to convert the qualitative variables in semi-quantitative criteria and to elaborate on normalization of attribute values and discretization of EIPN scores. Then polytomous logistic regression models quantified the link between the probability of a EIPN score and the variables of exposure. For research efficiency, future surveys should collect as few optimal variables as possible to obtain the EIPN score for blocks potentially around any populated, active volcano.

In the literature, the global-scale 'PEI' underestimates local factors that induce spatial and temporal exposure patterns. In contrast, our work highlights local factors that combine on a multi-component human exposure. Local factors that govern the extent to which settlements are exposed to specific threats involve: (1) topography and geomorphological location near valleys that convey most of the flows; (2) fatalities location related to hazard type and frequency, which affect human habitat and livelihoods, and; (3) home and roof quality that may increase exposure of residents. We consider exposure related to volcanic flows (PDCs, lahars) inasmuch these hazards induce impacts on settlements located near active river channels and adjacent low banks. We thus accounted for shallow and sinuous valley channels,
862 which favour overbank pyroclastic surges, overbank lahars and associated floods along the
863 most active rivers (Fig. 3). We do not discard outside hazard-prone areas affected in the recent
864 past by lahars and associated floods produced by overbank and avulsion into secondary
865 drainage. On the other hand, we also considered local factors that may contribute to decrease
866 exposure, e.g., easy access on paved roads to the dusun, short distance/time (≤ 5 km and ≤ 25
867 minutes by car depending on the quality of roads) to health centres, emergency facilities, and
868 finally the existence of civil protection works.

869 Measurements of exposure depends on the time frame at which assessments are conducted
870 (Auker et al., 2015). We examined exposure not only in primary residence (Wild et al., 2021),
871 but also in working areas: schools, shops, farmland and valleys, as many men extract
872 construction material from lahar and PDC deposits in active river channels. Here, the
873 ‘counting record’ of events and impacts held by the Dutch and Indonesian Volcanological
874 Survey since 1884 ensures completeness of the Semeru daily explosive activity and its
875 multiple chronic spurts. Monitoring from the Semeru Observatory at Gunung Sawur, 12 km
876 SE of the summit (Fig. 3) contributed to the eruption record as early as 1953.

877 Few studies address exposure of highly populated communities on a persistently active
878 volcano. Instead of sporadic (e.g., Mayon) or chronic eruptive activity (e.g., Merapi),

879 Semeru's acute and constant, daily explosive activity since at least 1967—the longest daily
880 explosive unrest worldwide with the exception of Sakurajima—exerts a heavy, constant toll on
881 human life, thus on perception and adaptation of communities exposed to volcanic risk.

882 8. CONCLUSION

883 The local-scale method, including field data collection, mapping and a range of statistical
884 techniques, helped compute four levels of a multi-component index of exposure applied to
885 145 neighbourhoods mapped in 13 *dusun* and two small towns.

886 Polytomous logistic regression models allowed us to select six optimal variables and predict
887 the EIPN score of blocks These optimal variables are reproducible parameters to assess
888 human exposure on active, populated volcanoes.

889 Computing and mapping human exposure at the block scale may be more adapted to: 1) the
890 characteristics of the population, (2) the diversity of hazards and timely change in exposure to
891 a persistently explosive activity, and (3) the mixed rural-small urban communities with a
892 variety of resources, which support livelihoods and sustain the community coping capacity.
893 Ranking blocks using distance-timing and access to response facilities is a useful tool to point
894 blocks that need relief operations to be implemented. Civil authorities may provide advice and
895 funds to retrofit home construction, relocate exposed homes that encroached on low terraces,
896 clean up check dams, pave access ways to, and implement dispensaries in remote villages.
897 Results should help disaster risk management staff to improve their participation at the scale
898 of neighbourhoods on active volcanoes.

899 Acknowledgments

900 Fieldwork and laboratory analyses were funded by the ANR ‘RiskAdapt’ research project.
901 This research was also financed by the French government IDEX-ISITE initiative 16-IDEX-
902 0001 (CAP 20-25). The authors are grateful to DIKTI (Directorate General of Higher
903 Education, Ministry of National Education of Indonesia), who bestowed two research permits
904 to the first author. We acknowledge the technical and scientific support from Dr. A.-F. Yao
905 Lafourcade (Laboratory of Mathematics, UCA), University Gadjah Mada, Yogjakarta (Isna
906 Pujiastuti) and University IPB, Bogor (Muhammed Syaif Habi, F. Muhammed A.W. Hasan).
907 We thank Mr. Mahjum and Pak Sam for their logistical support in field.

908 Declarations

909 *Authors' contribution statements.* All authors contributed to the study conception and design.
910 Material preparation, data collection and analysis were performed by JC Thouret, M
911 Taillandier, E Wavelet, N Azzaoui, and B Tjajhono. The first draft of the manuscript was
912 written by JC Thouret and M Taillandier and all authors commented on previous versions of
913 the manuscript. All authors read and approved the final manuscript. Artwork was performed
914 by M Taillandier, E. Wavelet, JC Thouret and O Santoni.

915 *Ethics approval.* The research did not involve Human participants and/or Animals

916 *Funding and/or Conflict of interests/Competing interests.* Funding (no specific grant received)
917 has been cited in the Acknowledgments. The authors have no competing interests to declare
918 that are relevant to the content of this article. All authors certify that they have no affiliations
919 with or involvement in any organization or entity with any financial interest or non-financial
920 interest in the subject matter or materials discussed in this manuscript.

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1092
1093 TABLE CAPTIONS

1094 **Table 1.** Methods, techniques and objectives of the statistical analyses (UA, BA, FCA, MCA
1095 and HAC) conducted on variables and attributes of the Exposure Index of Populated
1096 Neighbourhoods (EIPN). Observations are neighbourhoods termed blocks. Polytomous
1097 Logistic regression (PLR) is a model to extract most pertinent variables for predicting EIPN
1098 scores of blocks, which can be applied outside the survey area and on any populated, active
1099 volcano. HAC is a clustering technique to distinguish groups of variable attributes that
1100 describe a range of exposed blocks.

1101 **Table 2.** Variables and their attributes (with thresholds) used to compute the EIPN at the local
1102 *dusun* (sub-village) and block (neighbourhood) scale. **A.** Eleven variables define what can
1103 increase population exposure. **B.** Seven variables linked to hazard types, occurrence and
1104 frequency. **C.** Five variables describe accessibility to blocks and the existence/distance of
1105 response facilities in case of imminent eruption and evacuation. Ordinal numbers (without
1106 weighting) were attributed as thresholds of attributes within each variable, but different values
1107 for each attribute were normalized for computing the EIPN, while the index score intervals
1108 were discretized using the Jenks method. We calculated weighting coefficients associated to
1109 the variable no.4 using the method of intersection of probabilities adapted from Brown et al.
1110 (2015), see Table 3.

1111 **Table 3.** Four steps describing the method for computing the exposure index adapted from the
1112 People Exposure Index (PEI: Brown et al., 2015a) according to four circle distances (9, 12,
1113 18, and 35 km) around Semeru, taking into account the recorded volcanic events, village
1114 population, and reported fatalities since 1884.

1115 **Table 4.** A. PLR initial model with 8 variables and their attributes with coefficients of
1116 explanatory variables, significativity and thresholds of EIPN coefficients. B. PLR selected
1117 model showing the 13 attributes of 6 variables with their estimated coefficients and
1118 ‘significativity’. We selected the most significant variables (3 stars), meaning < 0.1% error in
1119 probabilities, although a less strict selection would also retain 2-stars attributes, i.e., with <
1120 1% error in probabilities.

1121 **Table 5.** Results of the Chi-square test conducted on the three groups of variables. The dashed
1122 black line indicates the results for the 11 exposure variables. Bold P-values indicate variables
1123 that are independent (no statistical relationship) because p-value exceeds 5% based on the fact
that a few sample cohorts do not exceed 5 observations (ESD Table 3). P-value, a probability number between 0 and 1, is defined as the probability of getting a result that is either the same or more extreme than the actual observations. Almost all p-values are significant for a threshold of 5%. Hence, we pose two hypotheses: two variables are independent if p-value exceeds 5% (i.e., the null hypothesis $H_0$), against the alternative $H_1$, i.e., two variables are dependent if p-value is < 5%. While taking account of the size of the table of contingency (number of freedom degree), the Chi-square test computes the deviation between observed and theoretical counts (i.e., those expected if two variables were independent). Upon assessing this deviation, the hypothesis of independence is accepted or rejected.

**Table 6.** Results from Hierarchical Analytical Clustering (HAC) showing four clusters or groups of blocks based on the frequency of attributes, as shown by the outputs of MCA. See Factor map, ESD Figure 4 that helps support the distinction between four clusters of blocks.

**Table 7.** Four clusters of at-risk blocks in *dusun* based on the distance/timing criterion, access to blocks and response facilities. See Factor map, ESD Figure 5 and ESD Table 9 that help support the distinction between four clusters of blocks in case of evacuation.

**Table 8.** Comparison of parameters used in the Exposure Index of Populated Neighbourhoods at Semeru with previous methods and parameters defining exposure within risk studies from the literature.

## FIGURE CAPTIONS

**Figure 1.** Flow chart showing four research steps and how we defined, computed and mapped an Exposure Index of Populated Neighbourhoods (EIPN) at the local scale of sub-village (*dusun*) blocks (neighbourhoods). The fourth research step is an application of the local scale method to identify remote and/or blocked blocks for imminent eruption including evacuation.

**Figure 2.** A. Map of Semeru volcano and ring plain in the middle of the regencies of Lumajang to the east and Malang to the west. Distance circles 9, 12, 18 and 35 km (dashed white lines) with respect to the persistently active vent. Principal cities and towns are indicated. Red circle in map B points to the Semeru-Tengger massif, East Java. M= Malang, S= Surabaya.

**Figure 3.** Hazard-zone map of the Semeru's slopes and ring plain depicting the extent of volcanic phenomena in the case of a medium-sized (VEI 3) eruption, based on the 1994, 1995
and 2002 eruptions and post-eruption lahars (Thouret et al., 2007). The map also shows the extent of lahars and floods in the case of a catastrophic eruption (VEI > 3) along valleys through the distal south and east ring plain, based on the 1909 and 1981 events and the disaster-prone areas map (Bronto et al., VSI, 1996). Initials indicate the surveyed 13 *dusun*: B-K Blubuk, Karangsuko, SU Supit, RB Rowobaung, SB Sumbersari, GM Gumuk Mas, CK Curah Koboan, KK KajarKuning, TU Tulungrejo, JA Jabon, SM Sumbermulyo, JL Jaranglangak, RE Rekesan, and two towns of Pronojiwo and Senduro.

**Figure 4.** Results of univariate analysis (given in percentage) conducted on 43 attributes of 11 EIPN variables applied to all studied *dusun* blocks. The grayscale block attributes, from the lowest exposure index (light grey) to the highest exposure index (dark grey), are similar for all plots.

**Figure 5.** Maps showing the EIPN score at the scale of each *dusun* block. A. Town of Pronojiwo, *Dusun* Supit-Supit Timur, and Rowobaung. B. *Dusun* Sumbersari, Gumuk Mas, Curah Lengkong, and Kajar Kuning on both sides of K. Koboan. C. *Dusun* Tulungrejo, Tawonsongo and Jabon in Desa Pasarjumbe. D. *Dusun* Blubuk and Karangsuko. E. *Dusun* Sumbermulyo, Juranglangak, and Rekesan in Desa Senduro. The four colour-coded final scores of the EIPN (obtained from ESD Table 6) show how one of the exposure index score levels were assigned to every block.

**Figure 6.** Scatter plots derived from MCA results showing the most opposed attributes (i.e., away from the plot barycentre) of 11 contributory variables for exposure along the Dimensions1 and 2 that convey 76% of the information.

**Figure 7.** Results of univariate analysis (given in percentage) conducted on all attributes of the second group and third group of variables applied to all studied *dusun* blocks. The seven hazard variables are V12 to V18. The five variables V19 to V23 describe access to blocks and the existence and distance of response facilities.

**Figure 8.** Results from Multiple Component Analysis (MCA) on variables and their attributes correlated to the EIPN score. A. Plot showing contribution of variables along Dimensions 1 and 2 (76% of the information). B. Plot showing contribution of variables along Dimensions 2 and 3 (23% of the information). Percentages of information are shown in ESD Figure 3.

**Figure 9.** Maps of four clusters of blocks in *dusun* shown in Figure 5 based on distance/timing criterion (including the quality of access and means of transport) to the
response facilities (shelters, storage facility, early warning system, BNPB offices and health centres). The four block clusters stem from the high/low frequency analysis (Table 7) and HAC Factor map (ESD Figure 5). A. Greeen: Easy access to blocks (10-25 minutes) and short distance response facilities (2.5–5 km). B. Yellow: Slow access to blocks (25 to 45 minutes) and short- and mid-distance response facilities (2.5–10 km). C. Orange: Challenging access to blocks (45 to 170 minutes) and long-distance (> 10 km) response facilities. D. Red: Delayed access to blocked blocks (80 to 170 minutes) and remote or absent response facilities.

## 1193 Electronic Supplement Data ESD

ESD Figure 1. Maps showing the setting of the 15 *dusun* together with the blocks in which we conducted field survey and statistical analyses on exposure parameters. A. Karangsuko and Blubuk (Desa Tamansatryan), west flank. B. Town of Pronojiwo, Supit-Supit Timur and Rowobaung (Desa Pronojiwo), South flank. C. Oro-Oro Ombo (Desa and *dusun*), Sumbersari, Gumuk Mas, Curah Lengkong (Desa Supit Urang), SSE and SE flank. D. Kajar Kuning (Desa Sumberwuluh) and Desa Candipuro, SSE flank. E. Tulungrejo and Jabon (Desa Pasrujambe), ESE flank, and F. Sumbermulyo, Juranglangak, and Rekesan (Desa Senduro), East flank.

ESD Figure 2. Scree model with distribution of information according to dimensions.

ESD Figure 3. Factor map obtained from HAC showing four groups of blocks based on attribute frequencies: see Table 6 for the list of high and low attribute frequencies.

ESD Figure 4. Factor map obtained from distance/timing criteria and HAC (Table 7) and showing four clusters of blocks according to access and response facilities.

ESD Table 1. Setting of surveys carried out in *dusun* (sub-villages): administrative units, location, surface area, people density, and number of surveys in each *dusun*. Symbol meaning: * data from BPS reports, *Kecamatan Dalam Angka* 2019, and 2018 for Tamansatryan, Sumberwuluh, Candipuro. **A dusun usually includes 4 to 5 *Rukun Warga* (RW, a neighbourhood with 50 to 75 houses). A RW includes usually 3 to 9 *Rukun Tetanga* (RT, a block with 20 to 25 houses). Field survey was carried out at the scale of RWs, including more than one observation per RT.

ESD Table 2. Coordinates of buildings, economic status of respondents, and geographical exposure with respect to active valleys.
1215 ESD Table 3. Chi-square test on the set of 23 variables to determine whether two variables are independent or dependent. In this case, a variable is independent if the p-value exceeds 5% (see Table 5). As a result, a statistical link exists (95% confidence) between variables indicated as dependent with corresponding variables listed in the first column. Dark grey indicates variables of exposure, grey variables of hazards, and white variables of access and response.

1221 ESD Table 4. Burt Table of contingency (all attributes are considered) showing statistical links between attributes of two variables at a time.

1223 ESD Table 5. Coordinates, squared cosine, and contribution of attributes used in MCA biplots.

1225 ESD Table 6. Master Table of computed EIPN per *dusun* blocks, totalling 145 (horizontal rows; two initials indicate the *dusun* name) according to all exposure variables and their attributes (vertical rows, see Table 3). A. The colour-coded final scores of the EIPN are displayed at the end of the Table as well as in Figure 5. All blocks delineated in Figure 5 A-C and D-F were attributed one of the colour-coded Exposure Index score levels.

1230 ESD Table 7. Confusion matrix of the selected PLR model. This Table crosses 'real' observed EIPN scores with predicted ones when we applied the model to the initial (observed) data (145 blocks). Grey boxes show well predicted EIPN values in contrast to yellow boxes indicating poorly predicted EIPN values.

1234 ESD Table 8. Chi² test on discriminant variables that support HAC clusters.

1235 ESD Table 9. Chi2 test on variables of timing, access and response that support block clusters for relief operation in case of imminent evacuation.

1237
<figure><img src="image_4.png" /></figure>

Clermont, September 24, 2022

Dr. James Goff, Editor in Chief,

Natural Hazards Journal

We would like to submit the revised article entitled: "*Semeru volcano, Indonesia: Measuring hazard, exposure and response of densely populated neighbourhoods facing persistent volcanic threats*" to Natural Hazards.

In response to your request, we apologize and we thank you for having drawn our attention to the short paragraphs that may be considered as plagiarism and needed to be reworked before peer review. We have revised the third paragraph in sections 1.1, the first and third paragraphs in section 1.2, the second paragraph in section 1.3, and finally the first paragraph in section 1.5. We have shortened and reworked the mentioned lines, indicated their sources and added citations where appropriate. In the case of sections 1.3 and 1.5, we have referred to the publication from which the lines have been borrowed. In fact, we borrowed the content of these lines from our publication "Thouret et al., 2022" (International Journal of Disaster Risk Reduction). We hope that the revision would meet your expectation before peer review.

The research presented in this article, which was initiated at Laboratoire Magmas et Volcans (LMV), Université Clermont-Auvergne (UCA) in France, has been conducted in collaboration with the Laboratoire de Mathématiques (also UCA), and with Indonesian partners of the IPB University, Bogor in the framework of the "RiskAdapt" research program funded by the French National Agency for Research (ANR).

The article shows the population exposure to Semeru's volcanic threats from its persistent, daily eruptive activity, which endangers at least 50,000 of the 950,000 inhabitants living on its slopes and ring plain. Surveys, mapping and statistical investigation enabled us to assess the extent of exposure of 145 neighbourhoods (termed blocks) and characterize hazards and response to eruptions in 15 rural villages and small towns. Univariate, bivariate and multivariate analyses were used to explore data and characterize the relationships between 11 variables to compute a multi-component exposure index. Logistic Regression models allowed to select six optimal exposure variables. Multivariate analyses and Hierarchical Agglomerative Clustering distinguished four groups of blocks based on attributes of all variables correlated to the exposure index score.

To contribute to disaster risk reduction, ranking blocks using distance-timing and access to response facilities is a useful tool to point remote or blocked blocks during imminent eruptions including evacuation. Results should help disaster risk management staff to improve their participation at the scale of neighbourhoods on active volcanoes.

We hope this article meets the scope and high standards of Natural Hazards. Thank you very much for your consideration.

Sincerely yours,

<figure><img src="image_5.png" /></figure>

Jean-Claude Thouret, First and corresponding author, Professor, Université Clermont Auvergne (UCA), OPGC, CNRS and IRD, Laboratoire Magmas et Volcans, F-63000 Clermont-Ferrand, France. Email: j-claude.thouret@uca.fr; Phone +33 4 73 34 67 73;
cell phone 33 6 25 19 41 17

Co-authors: Emeline Wavelet, MSc student at LMV, UCA; Marie Taillandier, MSc student Laboratoire de Mathématiques UCA; Boedi Tjahjono, Professor, Faculty of Agriculture, IPB University, Bogor, Indonesia; Nourddine Azzaoui, Professor, Laboratoire de Mathématiques UCA, and; Olivier Santoni (GIS engineer, FERDI also at UCA).
STATISTICAL METHOD APPLIED TO THE SURVEY DATASET

Uni-, Bivariate analyses, Factorial and Multiple Correspondence Analyses,
Hierarchical Agglomerative Clustering, Polytomous Logistic Regression

Measuring exposure, hazard and response of densely populated neighbourhoods
facing Semeru's persistent volcanic threats

<table><thead><tr><th>METHODS</th><th>OBSERVATIONS (23 variables, 145 blocks)</th><th>RESULTS</th></tr></thead><tbody><tr><td>Field surveys<br>Data acquisition<br><br>STEP 1. Computing the Index score to rank exposure of <i>dusun</i>* blocks (neighbourhoods)<br><br>Comparison between exposure parameters, and scale (global, local) of exposure analysis<br><br>STEP 2. Polytomous Logistic Regression models<br>Initial model: 8 variables<br>Selected model: 6 variables<br><br>STEP 3. Mapping blocks according to the Exposure Index score (EIPN)<br><br>STEP 4. Ranking and mapping at-risk blocks in case of imminent eruption</td><td><strong>Eleven variables that may increase exposure:</strong><br>V1. Village distance to vent<br>V2. Population density per village (or <i>dusun</i>*)<br>V3. Population density per built block area<br>V4. Prior fatalities/population/circle distance<br>V5. Fatalities location<br>V6. Lahar fatalities<br>V7. PDC fatalities<br>V8. Location of neighbourhoods from river channel<br>V9. Timely location of people<br>V10. Building exposure: orientation<br>V11. Building exposure: construction quality<br><br><strong>Six optimal variables from the set of 11 exposure variables:</strong><br>V1, V2, V3, V5, V6, V7<br><br><strong>HAZARD Level defined by 7 variables:</strong><br>V12. Volcanic Explosivity Index (VEI)<br>V13. Lahar occurrence<br>V14. Lahar frequency<br>V15. PDC occurrence<br>V16. PDC frequency<br>V17. Tephra-fall occurrence<br>V18. Tephra-fall frequency<br><br><strong>ACCESS to neighbourhoods and RESPONSE FACILITIES 5 variables:</strong><br>V19. Road access<br>V20. Evacuation shelter, road sign, and storage facility<br>V21. Civil defense work<br>V22. Early warning system and Civil protection office<br>V23. Health centres<br><br>Distance/timing criteria based on access ways and means of transport, and access to response facilities</td><td><strong>Statistical distribution and dependence between 11 variables</strong><br><br><strong>Exposure index (EIPN) score: four levels, from residual to very high</strong><br><br><strong>Gaps identified in literature on exposure definitions and parametrization</strong><br><br><strong>Modeling EIPN Prediction capacity outside the study area</strong><br><br><strong>Definition of blocks based on correlation of hazard level, access, and response facilities with EIPN score</strong><br><br><strong>Characterizing four groups among 145 blocks</strong><br><br><strong>Application to relief operations in case of evacuation</strong></td></tr></tbody></table>
<figure><img src="image_6.png" /><figcaption>Fig. 2</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Fig. 3</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Fig. 4</figcaption></figure>

Click here to access/download:Figure;Figure 4. Results of UA exposure (29.03.22).pdf

<figure><img src="image_9.png" /></figure>
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<table><caption>Figure 5 ABC</caption><caption>Exposure Index of Populated Neighbourhoods<br>Level, colour and score range</caption><thead><tr><th>EIPN</th><th>COLOUR</th><th>SCORE</th></tr></thead><tbody><tr><td>Residual</td><td>beige</td><td>1.17 - 3.45</td></tr><tr><td>Very low/Low</td><td>light blue</td><td>3.46 - 4.34</td></tr><tr><td>Moderate/High</td><td>yellow</td><td>4.35 - 5.34</td></tr><tr><td>Very high</td><td>red</td><td>5.35 - 6.50</td></tr></tbody></table>
<figure><img src="image_12.png" /></figure>
<caption>Figure 6</caption>

Click here to access/download;Figure;Figure 6. MCA Scatterplot axes 1-2 MT
(27.04.22).pdf

<figure><img src="image_13.png" /></figure>

Fig. 6
<figure><img src="image_14.png" /></figure>

V12. Volcanic Explosivity Index

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

V14. Lahar frequency

<figure><img src="image_17.png" /></figure>

V15. PDC occurrence

<figure><img src="image_18.png" /></figure>

V16. PDC frequency

<figure><img src="image_19.png" /></figure>

V17. Tephra-fall occurrence

<figure><img src="image_20.png" /></figure>

V18. Tephra-fall frequency

<figure><img src="image_21.png" /></figure>

V19. Road access

<figure><img src="image_22.png" /></figure>

V20. Evacuation shelter, road sign and storage facility

<figure><img src="image_23.png" /></figure>

V21. Civil Protection work

<figure><img src="image_24.png" /></figure>

V22. Early warning system and civil protection office

<figure><img src="image_25.png" /></figure>

V23. Health centres

<figure><img src="image_26.png" /></figure>
<figure><img src="image_27.png" /><figcaption>Fig. 8A</figcaption></figure>

<figure><img src="image_28.png" /><figcaption>Fig. 8B</figcaption></figure>
<figure><img src="image_29.png" /><figcaption>Figure 9</figcaption></figure>
<figure><img src="image_30.png" /></figure>

<table><thead><tr><th>Dataset and observations</th><th>Methods</th><th>Techniques</th><th>Purposes</th></tr></thead><tbody><tr><td>11 EIPN variables<br>7 Hazard variables<br>5 Access/Response variables</td><td>Univariate Analysis<br>UA</td><td>Frequency: bar plots</td><td>To obtain the frequency of all categories of every variable</td></tr><tr><td>11 EIPN variables<br>Dataset of all variables</td><td>Bivariate Analysis<br>BA</td><td>Chi-square test<br>Contingence tables: Burt Table<br>Biplot illustrations<br>Factorial Correspondence Analysis FCA</td><td>To identify dependence or independence between two variables<br>Allows to analyse the cross frequency between two variables<br>To show links between categories of two variables in biplots</td></tr><tr><td>Initial PLR model:<br>Choice of 8 variables among<br>11 initial EIPN variables</td><td>Polytomous<br>Logistic Regression<br>PLR</td><td>Backward and forward model selection<br>Comparison of deviance and Akaike Information<br>Criterion (AIC)</td><td>To select variables that are more relevant and discard less relevant ones. To reduce the number of optimal variables</td></tr><tr><td>Sorting out the 6 most<br>pertinent variables and 43<br>categories=<br>Final PLR Model for<br>predicting EIPN at the block<br>scale beyond the survey area,<br>potentially applicable to any<br>given active volcano</td><td>PLR models to<br>select pertinent<br>variables and<br>predict Exposure<br>Index of Populated<br>Neighbourhoods</td><td>Study of probability or significativity of the<br>categories<br><br>Explore the confusion matrix<br>Analysis of coefficients of the selected model<br><br>Predict function (Application)</td><td>To analyse the significativity of categories and select<br>variables that show the lowest probability of error.<br>To determine the predictive quality of the model.<br>To assess the strength of association between each active<br>variable and the variable to be explained (EIPN): positive<br>coefficient (i.e., increasing exposure) or negative coefficient<br>(i.e., decreasing exposure).<br>To compute the probability to obtain EIPN score for a block.<br>To measure the effect of an explanatory variable on EIPN.<br>Ultimate goal: to select optimal variables used around any<br>similarly populated, active volcano.</td></tr><tr><td>Set of 23 variables:<br>- Illustrative variables (e.g.,<br>EIPN score)<br>- Active variables: V1...V23<br>(see Table 3)</td><td>Multiple<br>Correspondence<br>Analysis MCA</td><td>Study of eigenvalues (Benzécri correction)<br>Graphic illustrations: ellipses<br><br>Fisher (variables) and Student (categories) tests<br><br>Study and projection of observations (e.g.,<br>blocks) in biplots</td><td>To define and describe the suitable number of clusters<br>To determine proximities and oppositions between categories<br>of one variable in the projection of blocks<br>To determine correlations of variables along each dimension<br>and coordinates of categories<br>To establish groups of observations that provide more<br>contribution and having similar behaviour. To analyse links<br>between different categories.</td></tr><tr><td>Outputs of two MCAs<br>conducted<br>on 23 variables</td><td>Hierarchical<br>Agglomerative<br>Clustering HAC</td><td>Dendrograms and Factor maps<br>Chi-square test<br>Analysis of clusters</td><td>To define and describe the suitable number of clusters.<br>To identify variables which discriminate clusters.<br>To identify discriminant categories and their frequencies.</td></tr><tr><td>Group of 5 access/response<br>variables and distance/time</td><td>Frequency analysis<br>HAC clusters</td><td>Distance/time parameter extracted from<br>frequency classification</td><td>To identify the remote and landlocked blocks in case of<br>imminent eruption and evacuation.</td></tr></tbody></table>
<table><caption>Table 3</caption><thead><tr><th>Computing steps</th><th>Variables</th><th colspan="4">Thresholds</th><th>Comments</th></tr></thead><tbody><tr><td rowspan="5">First step</td><td>Distance, km</td><td>&lt; 9</td><td>12</td><td>18</td><td>35</td><td>Events / fatalities for 1884 - 2014</td></tr><tr><td>Volcanic EVENTS</td><td>7</td><td>9</td><td>9</td><td>9</td><td>Total events with 25 fatalities</td></tr><tr><td>Weighting</td><td>0.206</td><td>0.265</td><td>0.265</td><td>0.265</td><td></td></tr><tr><td>Reported FATALITIES</td><td>10</td><td>595</td><td>255</td><td>255</td><td>1884-2014: 1115 victims (7/year), Table 2, Thouret et al. (2007)</td></tr><tr><td>Weighting</td><td>0.009</td><td>0.534</td><td>0.229</td><td>0.229</td><td></td></tr><tr><td rowspan="5">Second step</td><td>DISTANCE CIRCLE</td><td>9-km circle</td><td>12-km circle</td><td>18-km circle</td><td>35-km circle</td><td>Circle radius around the Semeru crater</td></tr><tr><td>No. of villages</td><td>2 Dusun</td><td>Dusun except Supit</td><td>other</td><td>None in surveys</td><td></td></tr><tr><td>POPULATION</td><td>2,864</td><td>59,236</td><td>85,303</td><td>842,597</td><td>Total 35-km half circle E, S and SW slopes: c. 843,000 people</td></tr><tr><td>Weighting</td><td>0.003</td><td>0.060</td><td>0.086</td><td>0.851</td><td></td></tr><tr><td>SURFACE AREA</td><td>9 km radius</td><td>12</td><td>18</td><td>35</td><td>Circle surface area</td></tr><tr><td rowspan="7">Third step</td><td>2/3 of the SA (East)</td><td>169.646</td><td>301.593</td><td>678.584</td><td>2565.634</td><td>Total 4 areas</td></tr><tr><td colspan="6">The 9 km area is 1.778 times smaller than the 12 km area: 12/9 km= 1.778</td></tr><tr><td colspan="6">The 9 km area 4.00 times smaller than the 18 km area; 18/9 km= 4.000</td></tr><tr><td colspan="6">The 9 km area 15.123 times smaller than the 35 km area; 35/9 km= 15.123</td></tr><tr><td colspan="6">Total circles = 9 km/9 km +12 km/9 km+ 18 km/9 km+35 km/9 km= 21.901</td></tr><tr><td colspan="6">In the 9 km-distance circle we obtain 1/21.901= 0.046</td></tr><tr><td colspan="6">In the 12 km-distance circle we obtain 1.778/21.901= 0.081</td></tr><tr><td rowspan="5">Fourth step</td><td colspan="6">In the 18 km-distance circle we obtain 4.00/21.901= 0.183</td></tr><tr><td colspan="6">In the 35 km-distance circle we obtain 15.123/21.901 = 0.691</td></tr><tr><td colspan="6">Two sets of weights are combined and scaled (scaled= with respect to the total circle)</td></tr><tr><td>Weighting</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>&le; 9-km circle</td><td></td><td>0.28*0.046/(Total) = 0.206*0.046 / 0.263= 0.036</td><td></td><td></td><td></td></tr><tr><td>12- km circle</td><td></td><td>(0.36*0.081) / 0.356= 0.082</td><td></td><td></td><td></td></tr><tr><td>18- km circle</td><td></td><td>(0.36*0.183) / 0.356= 0.185</td><td></td><td></td><td></td></tr><tr><td>35- km circle</td><td></td><td>(0.36*0.691) / 0.356= 0.698</td><td></td><td></td><td></td></tr><tr><td></td><td></td><td>Total=0.206*0.046 +0.265*0.081 +0.265*0.183 +0.265*0.691 = 0.263</td><td></td><td></td><td>Coefficients used in Table 3</td></tr><tr><td colspan="6">Population within each circle distance category is multiplied by weighting and the four figures are summed</td></tr><tr><td>&lt;9 km-distance circle</td><td></td><td>2,864 x 0.036= 103</td><td></td><td></td><td></td></tr><tr><td>12 km-distance circle</td><td></td><td>59,236 x 0.082= 4,858</td><td></td><td></td><td></td></tr><tr><td>18 km-distance circle</td><td></td><td>85,303 x 0.185= 15,739</td><td></td><td></td><td></td></tr><tr><td>35 km-distance circle</td><td></td><td>842,597 x 0.698= 587,803</td><td></td><td></td><td></td></tr><tr><td></td><td></td><td>Sum= 608,502</td><td></td><td></td><td></td></tr><tr><td colspan="6">PEI values attributed to population categories per circles:</td></tr><tr><td></td><td>Population</td><td>Rounded population</td><td>PEI</td><td>Circle radius distance, km</td><td>Smaller than</td></tr><tr><td></td><td>103</td><td>105</td><td>1</td><td>9</td><td>x 1</td></tr><tr><td></td><td>4,858</td><td>5,000</td><td>2</td><td>12</td><td>x 1.778</td></tr><tr><td></td><td>15,739</td><td>16,000</td><td>3</td><td>18</td><td>x 4.000</td></tr><tr><td></td><td>587,803</td><td>590,000</td><td>4</td><td>35</td><td>x 15.123</td></tr><tr><td></td><td>&gt; 608,502</td><td>&gt; 608,502</td><td>5</td><td>&gt; 35</td><td></td></tr></tbody></table>
<table><caption>Coefficients of the selected categories</caption><thead><tr><th>Variable</th><th>Category</th><th>Estimate</th><th>Std. Error</th><th>Z value</th><th>Pr(&gt;|z|)</th><th>Significat.</th></tr></thead><tbody><tr><td>V1 dist</td><td>9 to 12 km</td><td>5.90</td><td>2.53</td><td>2.33</td><td>0.02</td><td>*</td></tr><tr><td>V1</td><td>12 to 20 km</td><td>9.70</td><td>2.94</td><td>3.30</td><td>0.00</td><td>***</td></tr><tr><td>V2 densD</td><td>824 to 1090</td><td>2.28</td><td>1.25</td><td>1.82</td><td>0.07</td><td>.</td></tr><tr><td>V2</td><td>349 to 823</td><td>1.76</td><td>1.29</td><td>1.37</td><td>0.17</td><td></td></tr><tr><td>V2</td><td>&lt; 348</td><td>4.99</td><td>1.48</td><td>3.37</td><td>0.00</td><td>***</td></tr><tr><td>V2</td><td>0 (temp.)</td><td>34.71</td><td>7.37</td><td>4.71</td><td>2.47e-06</td><td>***</td></tr><tr><td>V3 densB</td><td>1 341 to 1 850</td><td>0.43</td><td>1.38</td><td>0.31</td><td>0.76</td><td></td></tr><tr><td>V3</td><td>891 to 1 340</td><td>3.05</td><td>1.01</td><td>3.01</td><td>0.00</td><td>**</td></tr><tr><td>V3</td><td>360 to 890</td><td>7.71</td><td>1.53</td><td>4.99</td><td>6.15e-07</td><td>***</td></tr><tr><td>V3</td><td>0 (temp.)</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td></td></tr><tr><td>V5 locat</td><td>Valley channel</td><td>7.25</td><td>3.27</td><td>2.21</td><td>0.03</td><td>*</td></tr><tr><td>V5</td><td>Interfluve</td><td>11.88</td><td>4.00</td><td>2.97</td><td>0.00</td><td>**</td></tr><tr><td>V5</td><td>No death reported</td><td>14.77</td><td>4.26</td><td>3.46</td><td>0.00</td><td>***</td></tr><tr><td>V6 LH</td><td>Yes</td><td>-9.47</td><td>4.47</td><td>-2.12</td><td>0.03</td><td>*</td></tr><tr><td>V7 PDC</td><td>Yes</td><td>-8.27</td><td>2.19</td><td>-3.78</td><td>0.00</td><td>***</td></tr><tr><td>V8</td><td>Low terrace - Short distance</td><td>-0.16</td><td>4.12</td><td>-0.04</td><td>0.97</td><td></td></tr><tr><td>V8</td><td>Low terrace - Middle distance</td><td>-1.78</td><td>9.97</td><td>-0.18</td><td>0.86</td><td></td></tr><tr><td>V8</td><td>Middle terrace - Short distance</td><td>-0.61</td><td>2.77</td><td>-0.22</td><td>0.83</td><td></td></tr><tr><td>V8</td><td>Middle terrace - Middle distance</td><td>-0.68</td><td>3.30</td><td>-0.21</td><td>0.84</td><td></td></tr><tr><td>V8</td><td>High terrace - Short distance</td><td>4.23</td><td>2.40</td><td>1.76</td><td>0.08</td><td>.</td></tr><tr><td>V8</td><td>High terrace - Middle distance</td><td>5.57</td><td>2.53</td><td>2.20</td><td>0.03</td><td>*</td></tr><tr><td>V8</td><td>High terrace - Long distance</td><td>6.73</td><td>2.72</td><td>2.47</td><td>0.01</td><td>*</td></tr><tr><td>V9</td><td>Temporary shelter/shop</td><td>1.14</td><td>1.36</td><td>0.84</td><td>0.40</td><td></td></tr><tr><td>V9</td><td>Permanent home</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td></td></tr></tbody></table>

<table><caption>Threshold coefficients</caption><thead><tr><th></th><th>Estimate</th><th>Std.</th><th>Z value</th></tr></thead><tbody><tr><td>Very high | Moderate - High</td><td>17.87</td><td>4.73</td><td>3.78</td></tr><tr><td>Moderate - High | Very low - Low</td><td>28.28</td><td>6.03</td><td>4.69</td></tr><tr><td>Very low - Low | Residual</td><td>35.73</td><td>6.80</td><td>5.25</td></tr></tbody></table>

Table 4A. Initial model for polytomous logistic regression.

<table><caption>Coefficients of the selected categories</caption><thead><tr><th>Variable</th><th>Category</th><th>Estimate</th><th>Std. Error</th><th>Z value</th><th>Pr(&gt;|z|)</th><th>Significat.</th></tr></thead><tbody><tr><td>V1 dist</td><td>9 to 12 km</td><td>4.81</td><td>2.06</td><td>2.34</td><td>0.02</td><td>*</td></tr><tr><td>V1</td><td>12 to 20 km</td><td>9.06</td><td>2.43</td><td>3.73</td><td>0.00</td><td>***</td></tr><tr><td>V2 densD</td><td>824 to 1090</td><td>3.50</td><td>0.92</td><td>3.81</td><td>0.00</td><td>***</td></tr><tr><td>V2</td><td>349 to 823</td><td>3.85</td><td>1.07</td><td>3.60</td><td>0.00</td><td>***</td></tr><tr><td>V2</td><td>&lt; 348</td><td>6.19</td><td>1.34</td><td>4.61</td><td>4.13e-06</td><td>***</td></tr><tr><td>V2</td><td>0 (temp.)</td><td>25.83</td><td>4.94</td><td>5.23</td><td>1.68e-07</td><td>***</td></tr><tr><td>V3 densB</td><td>1 341 to 1 850</td><td>-0.90</td><td>1.06</td><td>-0.85</td><td>0.39</td><td></td></tr><tr><td>V3</td><td>891 to 1 340</td><td>1.88</td><td>0.69</td><td>2.72</td><td>0.01</td><td>**</td></tr><tr><td>V3</td><td>360 to 890</td><td>5.39</td><td>1.13</td><td>4.77</td><td>1.87e-06</td><td>***</td></tr><tr><td>V3</td><td>0 (temp.)</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td></td></tr><tr><td>V5 locat</td><td>Valley channel</td><td>7.12</td><td>2.79</td><td>2.55</td><td>0.01</td><td>*</td></tr><tr><td>V5</td><td>Interfluve</td><td>12.25</td><td>3.48</td><td>3.52</td><td>0.00</td><td>***</td></tr><tr><td>V5</td><td>No death reported</td><td>15.45</td><td>3.72</td><td>4.16</td><td>3.22e-05</td><td>***</td></tr><tr><td>V6 Lahar</td><td>Yes</td><td>-7.20</td><td>2.52</td><td>-2.86</td><td>0.00</td><td>**</td></tr><tr><td>V7 PDC</td><td>Yes</td><td>-5.58</td><td>1.42</td><td>-3.94</td><td>8.05e-05</td><td>***</td></tr></tbody></table>

<table><caption>Threshold coefficients</caption><thead><tr><th></th><th>Estimate</th><th>Std. Error</th><th>Z value</th></tr></thead><tbody><tr><td>Very high | Moderate - High</td><td>13.86</td><td>3.86</td><td>3.59</td></tr><tr><td>Moderate - High | Very low - Low</td><td>21.43</td><td>4.62</td><td>4.64</td></tr><tr><td>Very low - Low | Residual</td><td>27.96</td><td>4.18</td><td>5.40</td></tr></tbody></table>

Table 4B. Selected model for polytomous logistic regression
<table><thead><tr><th></th><th>V1</th><th>V2</th><th>V3</th><th>V4</th><th>V5</th><th>V6</th><th>V7</th><th>V8</th><th>V9</th><th>V10</th><th>V11</th><th>V12</th><th>V13</th><th>V14</th><th>V15</th><th>V16</th><th>V17</th><th>V18</th><th>V19</th><th>V20</th><th>V21</th><th>V22</th><th>V23</th></tr></thead><tbody><tr><td>V1</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td><strong>0.062</strong></td><td>0.005</td><td>0.000</td><td><strong>0.301</strong></td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.004</td><td>0.040</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.001</td><td>0.000</td><td>0.014</td><td>0.000</td><td>0.000</td></tr><tr><td>V2</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.002</td><td>0.000</td><td>0.000</td></tr><tr><td>V3</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.006</td><td>0.000</td><td>0.000</td></tr><tr><td>V4</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td><strong>0.061</strong></td><td>0.005</td><td>0.000</td><td><strong>0.303</strong></td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.004</td><td>0.041</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.001</td><td>0.000</td><td>0.016</td><td>0.000</td></tr><tr><td>V5</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td><strong>0.227</strong></td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td><strong>0.431</strong></td><td>0.001</td><td>0.000</td><td>0.002</td><td><strong>0.647</strong></td><td>0.000</td></tr><tr><td>V6</td><td><strong>0.061</strong></td><td>0.000</td><td>0.000</td><td><strong>0.060</strong></td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.005</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td><strong>0.658</strong></td><td><strong>0.141</strong></td><td>0.000</td><td>0.045</td><td><strong>1</strong></td><td><strong>0.078</strong></td></tr><tr><td>V7</td><td>0.005</td><td>0.000</td><td>0.000</td><td>0.005</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td><strong>0.102</strong></td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td><strong>0.582</strong></td><td>0.011</td><td>0.000</td><td><strong>0.187</strong></td><td><strong>0.766</strong></td><td>0.005</td></tr><tr><td>V8</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td><strong>0.279</strong></td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td></tr><tr><td>V9</td><td><strong>0.304</strong></td><td>0.000</td><td>0.000</td><td><strong>0.302</strong></td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.001</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td><strong>0.736</strong></td><td><strong>0.391</strong></td><td>0.000</td><td><strong>0.237</strong></td><td><strong>0.162</strong></td><td><strong>0.138</strong></td></tr><tr><td>V10</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.002</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.025</td><td>0.000</td><td>0.000</td><td><strong>0.723</strong></td><td><strong>0.357</strong></td><td>0.011</td></tr><tr><td>V11</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td><strong>0.205</strong></td><td><strong>0.090</strong></td><td>0.028</td></tr><tr><td>V12</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td><strong>0.231</strong></td><td>0.005</td><td><strong>0.107</strong></td><td>0.000</td><td>0.002</td><td>0.002</td><td>0.000</td><td>0.000</td><td>0.015</td><td>0.003</td><td>0.000</td><td>0.000</td><td>0.013</td><td>0.000</td><td>0.000</td><td>0.003</td><td>0.000</td><td>0.000</td></tr><tr><td>V13</td><td>0.004</td><td>0.000</td><td>0.000</td><td>0.004</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.015</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td><strong>0.265</strong></td><td>0.015</td><td>0.000</td><td><strong>0.509</strong></td><td><strong>0.022</strong></td><td>0.026</td></tr><tr><td>V14</td><td>0.043</td><td>0.000</td><td>0.000</td><td>0.041</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.003</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td><strong>0.514</strong></td><td><strong>0.148</strong></td><td>0.000</td><td><strong>0.174</strong></td><td><strong>0.219</strong></td><td><strong>0.106</strong></td></tr><tr><td>V15</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td><strong>0.505</strong></td><td>0.000</td><td>0.000</td><td><strong>0.052</strong></td><td>0.000</td><td>0.000</td></tr><tr><td>V16</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td></tr><tr><td>V17</td><td>0.000</td><td>0.000</td><td>0.004</td><td>0.000</td><td><strong>0.435</strong></td><td><strong>0.656</strong></td><td><strong>0.580</strong></td><td><strong>0.275</strong></td><td><strong>0.746</strong></td><td>0.024</td><td>0.001</td><td>0.013</td><td><strong>0.271</strong></td><td><strong>0.504</strong></td><td><strong>0.521</strong></td><td><strong>0.122</strong></td><td>0.000</td><td>0.000</td><td>0.006</td><td>0.007</td><td><strong>0.600</strong></td><td>0.000</td></tr><tr><td>V18</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.001</td><td><strong>0.140</strong></td><td>0.012</td><td>0.000</td><td><strong>0.383</strong></td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.014</td><td><strong>0.147</strong></td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.003</td><td>0.000</td><td>0.038</td><td>0.000</td></tr><tr><td>V19</td><td>0.001</td><td>0.000</td><td>0.000</td><td>0.001</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.008</td><td>0.002</td><td>0.000</td><td>0.036</td><td>0.017</td><td><strong>0.063</strong></td></tr><tr><td>V20</td><td>0.000</td><td>0.000</td><td>0.006</td><td>0.000</td><td>0.001</td><td>0.045</td><td><strong>0.190</strong></td><td>0.000</td><td><strong>0.242</strong></td><td><strong>0.722</strong></td><td><strong>0.201</strong></td><td>0.003</td><td><strong>0.505</strong></td><td><strong>0.168</strong></td><td><strong>0.050</strong></td><td>0.000</td><td>0.007</td><td>0.000</td><td>0.035</td><td>0.000</td><td>0.033</td><td>0.000</td></tr><tr><td>V21</td><td>0.013</td><td>0.001</td><td>0.000</td><td>0.013</td><td><strong>0.648</strong></td><td><strong>1</strong></td><td><strong>0.767</strong></td><td>0.000</td><td><strong>0.162</strong></td><td><strong>0.355</strong></td><td><strong>0.083</strong></td><td>0.000</td><td>0.020</td><td><strong>0.225</strong></td><td>0.000</td><td>0.000</td><td><strong>0.603</strong></td><td>0.035</td><td>0.018</td><td>0.035</td><td>0.000</td><td>0.014</td></tr><tr><td>V22</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td><strong>0.075</strong></td><td>0.005</td><td>0.000</td><td><strong>0.136</strong></td><td>0.011</td><td>0.030</td><td>0.000</td><td>0.027</td><td><strong>0.113</strong></td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td><strong>0.060</strong></td><td>0.000</td><td>0.015</td><td>0.000</td></tr><tr><td>V23</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td><strong>0.131</strong></td><td><strong>0.675</strong></td><td><strong>0.167</strong></td><td>0.019</td><td><strong>0.724</strong></td><td><strong>0.143</strong></td><td>0.001</td><td>0.000</td><td><strong>0.308</strong></td><td>0.001</td><td><strong>0.224</strong></td><td>0.017</td><td>0.000</td><td>0.000</td><td>0.002</td><td>0.000</td><td><strong>0.515</strong></td><td>0.000</td></tr></tbody></table>

V1: Distance of village to vent

V2: Density of sub-village

V3: Density of built block area

V4: Fatalities/village population/circle distance

V5: Location of fatalities

V6: Lahar fatalities

V7: PDC fatalities

V9: Timely location of people

V10: House orientation with respect to flow

V11: Roof and construction quality

V12: Volcanic Explosivity Index

V13: Lahars

V14: Lahar frequency

V15: PDCs

V16: PDC frequency

V17: Tephra-fall

V18: Tephra-fall frequency

V19: Access ways

V20: Evacuation shelter, evacuation sign and storage facility

V21: Civil Defense work

V22: Early warning system and Civil protection office

<caption>Table 5</caption>
<table><caption>Table 6</caption><thead><tr><th></th><th>Cluster 1</th><th>Cluster 2</th><th>Cluster 3</th><th>Cluster 4</th></tr></thead><tbody><tr><td></td><td colspan="4" style="text-align: center;">The most frequent attributes</td></tr><tr><td>EXPOSURE</td><td>V2. Density of sub-village: 0 (temp.)<br>V3. Density of built block: 0 (temp.)<br>V6. Lahar fatalities: Yes<br>V7. PDC fatalities: Yes<br>V9. Timely location of people:<br>Temporary shelter/shop<br>V10. House orientation: Un-built area<br>V11. Roof and construction quality: Un-built area</td><td><strong>EIPN score: Residual</strong><br>V1. Distance to vent: [12, 20] km<br>V2. Density of sub-village: [824, 1090]<br>V3. Density of built block: [891, 1340]</td><td><strong>EIPN score: Moderate - High</strong><br>V1. Distance to vent: [9, 12] km<br>V2. Density of sub-village: [1091, 1690]<br>V3. Density of built block: [1851, 2650]<br>V9. Timely location of people: Permanent home</td><td><strong>EIPN score: Very low - Low</strong><br>V1. Distance to vent: &lt; 9km<br>V2. Density of sub-village: [349, 823]<br>V3. Density of built block: [360, 890]<br>V11. Roof and construction quality: Poor</td></tr><tr><td>HAZARDS</td><td>V13. Lahar occurrence: DF and/or HCF<br>V15. PDC occurrence: Confined flow and surge</td><td>V12 VEI: High VEI &gt; 3<br>V18. Tephra fall frequency: 5 to 25 years</td><td>V15. PDC occurrence: Surge<br>V18. Tephra fall frequency: 1 to 3 years</td><td>V17. Tephra fall: Lapilli & ash (dist &lt; 9 km)<br>V18. Tephra fall frequency: &lt; 1 year</td></tr><tr><td>RESPONSE</td><td>V19 Access way: Trail</td><td>V20. Evacuation shelter, evacuation sign and storage facility: No<br>V22. Early warning system and BPBD office: None</td><td>V20. Evacuation shelter, evacuation sign and storage facility: Yes &lt; 5 km<br>V22. Early warning system and BPBD office: Near (&lt; 2.5km)</td><td>V22. Early warning system and BPBD office: None<br>V23. Health Centres: None</td></tr><tr><td></td><td colspan="4" style="text-align: center;">The least frequent attributes</td></tr><tr><td>EXPOSURE</td><td>V5. Fatalities location with respect to valley: No reported<br>V6. Lahar fatalities: No<br>V7. PDC fatalities: No<br>V9. Timely location of people: Permanent<br>V11. Roof and construction quality: Regular</td><td>V1. Distance to vent: [9, 12] km</td><td><strong>EIPN score: Residual</strong><br>V2. Density of sub-village: 0 (temp.)<br>V3. Density of built block: 0 (temp.)<br>V10. House orientation: Un-built area<br>V11. Roof and construction quality: Un-built area</td><td>V1. Distance to vent: [9, 12] km</td></tr><tr><td>HAZARDS</td><td>V13. Lahar occurrence: None<br>V14. Frequency: None<br>V15. PDC occurrence: Surge</td><td>V18. Tephra fall frequency: 1 to 3 years</td><td>V12. VEI: High VEI &gt; 3<br>V18. Tephra fall frequency: 5 to 25 years</td><td>V17. Tephra fall: Ashfall &gt; 8 km<br>V18. Tephra fall frequency: 1 to 3 years</td></tr><tr><td>RESPONSE</td><td>V19 Access way: Paved road</td><td>V20. Evacuation shelter, evacuation sign and storage facility: Yes &lt; 5 km<br>V22. Early warning system and BPBD office: Near (&lt; 2.5 km)</td><td>V20. Evacuation shelter, evacuation sign and storage facility: No<br>V22. Early warning system and BPBD office: None</td><td>V20. Evacuation shelter, evacuation sign and storage facility: Yes &lt; 5 km<br>V22. Early warning system and BPBD office: Near (&lt; 2.5 km)<br>V23. Health Centres: Hospital (&lt; 10 km)</td></tr></tbody></table>
<table><thead><tr><th></th><th>Cluster 1</th><th>Cluster 2</th><th>Cluster 3</th><th>Cluster 4</th></tr></thead><tbody><tr><td></td><td colspan="4">The most frequent attributes</td></tr><tr><td>TIMING</td><td>Timing to access shelter: 10 minutes<br>Timing to access Health centres: 10 to 25 min.</td><td>Timing to access Health centres: 45 min.<br>Timing to access shelter: 25 min.</td><td>Timing to access shelter: 170 minutes<br>Timing to access Health centres: 45 to 170 min.</td><td>Timing to access Health centres: 80 to 170 min.<br>Timing to access shelter: 170 min.</td></tr><tr><td>RESPONSE</td><td>V19 Access road: Paved road<br>V20. Evacuation shelter, evacuation sign and storage facility: Yes ≤ 5 km<br>V22. Early warning system and BPBD office: Near ≤ 2.5 km<br>V23. Health Centres: Dispensary ≤ 5 km</td><td>V19. Access road: Dirt road<br>V21. Civil protection work: Present<br>V23. Health Centres: Hospital ≤ 10 km<br>V22. Early warning system and BPBD office: Near ≤ 2.5 km</td><td>V22. Early warning system and BPBD office: None<br>V20. Evacuation shelter, evacuation sign and storage facility: None<br>V19 Access road: Paved road</td><td>V19 Access road: Trail<br>V21. Civil protection work: Absent<br>V22. Early warning system and BPBD office: Near ≤ 2.5 km</td></tr><tr><td></td><td colspan="4">The least frequent attributes</td></tr><tr><td>TIMING</td><td>Timing to access shelter: ≥ 170 minutes<br>Timing to access Health centres: 45 min.</td><td>Timing to access shelter: 10 min.<br>Timing to access Health centres: 25 min.</td><td>Timing to access shelter: 10 min.<br>Timing to access Health centres: 45 min.</td><td>Timing to access Health centres: 25 to 45 min.<br>Timing to access shelter: 10 to 25 min.</td></tr><tr><td>RESPONSE</td><td>V20. Evacuation shelter, evacuation sign and storage facility: None<br>V19. Access road: Dirt road<br>V22. Early warning system and BPBD office: None</td><td>V19 Access road: Paved road or Trail</td><td>V22. Early warning system and BPBD office: Near ≤ 2.5 km<br>V20. Evacuation shelter, evacuation sign and storage facility: Yes ≤ 5 km<br>V23. Health Centres: Dispensary ≤ 5 km<br>V19 Access road: Trail</td><td>V19 Access road: Paved or Dirt road</td></tr></tbody></table>

Distance/timing criterion according to access ways and means of transport. The distance/time criterion displays three situations: the darker grey is, the longer it takes to access any given remote block.

<table><caption>Table 7</caption><thead><tr><th rowspan="2">Distance, in km from block to shelter or health facilities</th><th colspan="3">Timing, in minutes according to access way type and means of transport</th></tr><tr><th>Paved road (common car)</th><th>Dirt Road (4x4 vehicle or truck)</th><th>Trail (walk)</th></tr></thead><tbody><tr><td>2.5</td><td>5</td><td>15</td><td>40</td></tr><tr><td>5</td><td>10</td><td>25</td><td>80</td></tr><tr><td>10</td><td>25</td><td>45</td><td>170</td></tr></tbody></table>
<table><caption>Table 8</caption><thead><tr><th>Reference</th><th>Yokoya<br>ma et al.,<br>1984</th><th>Ewert et<br>al., 2005;<br>Ewert,<br>2007</th><th>Wood &<br>Soulard,<br>2009</th><th>Kinvig et<br>al., 2010</th><th>Aspinall<br>et al., 2011</th><th>Aucker<br>et al.,<br>2015</th><th>Brown et<br>al., 2015</th><th>Scandone et<br>al., 2016</th><th>Mangan et<br>al., 2018;<br>Ewert et al.,<br>2018</th><th>Del Negro et<br>al., 2020</th><th>Nieto-Torres<br>et al., 2021</th><th>This work</th></tr></thead><tbody><tr><td>Objective</td><td>Threat</td><td>Threat</td><td>Exposure of<br>communities<br>facing lahars</td><td>Threat</td><td>Risk</td><td>Risk</td><td>Threat<br>(country<br>level)</td><td>VolcanoRisk<br>Coefficient<br>VRC</td><td>VTA<br>Volcanic<br>Threat<br>Assessment</td><td>Risk Lava<br>flow<br>AHP</td><td>VRR Volcano<br>Risk Ranking</td><td>Human<br>exposure at<br>local scale</td></tr><tr><td>Method<br>and<br>indicator</td><td>Σ(H+E)</td><td>H*E</td><td>Composite<br>index for<br>lahar-prone<br>zones</td><td>NVEWS<br>Score<br>system<br>Nisyros<br>Island<br>and USA</td><td>H*PEI<br>Global risk<br>for<br>volcanic<br>countries</td><td>VHI*<br>PEI<br>Global</td><td>Mean<br>VHI* #<br>volcanoes*<br>pop30</td><td>K<sub>T</sub> eruption<br>between 0 & 1<br>year ago; VEI;<br>Log popul.</td><td>Surveys<br>California<br>and USA</td><td>GIS, AHP<br>Analytic<br>Hierarchy<br>Process</td><td>VRR(1)=H*E<br>*V<br>VRR(2)=(H*E<br>*V) /( res+1)</td><td>EIPN: field<br>survey,<br>mapping &<br>statistics</td></tr><tr><td>Scale</td><td>Global</td><td></td><td>Mt Rainier</td><td></td><td></td><td></td><td>Global</td><td></td><td>Etna volcano</td><td>Global</td><td>Local</td><td></td></tr><tr><td>Exposure</td><td colspan="13">Number of parameters evaluated for computing the Exposure factor</td></tr><tr><td></td><td>7</td><td>9</td><td>6</td><td>10</td><td>1= PEI</td><td>1= PEI</td><td>1= Total<br>popul. ≤ 30<br>km from<br>active<br>volcanoes<br>(pop30) by<br>country</td><td>1= Log<br>number of<br>popul.</td><td>9</td><td>1= density of<br>population<br>community</td><td>9</td><td>Initial: 11<br>Tested:8<br>Final: 6</td></tr><tr><td>Population<br>exposure</td><td colspan="13">Number and/or nature of parameters specifically evaluated for computing the Population Exposure Index</td></tr><tr><td></td><td>2:<br>Number<br>of<br>popul.;<br>Density</td><td>3=<br>popul. in<br>hazard<br>zones ≤<br>30 km<br>radius;<br>Past<br>fatalities<br>and<br>evacu-<br>ations;<br>aircrafts</td><td>3=<br>Residents;<br>Employees;<br>Dependent-<br>population<br>facilities</td><td>4=<br>Log10<br>volcano<br>popul.<br>≤30 km<br>and > 30<br>km; prior<br>fatalities,<br>prior<br>evacuatio<br>ns</td><td>2=<br>Number of<br>popul. per<br>distance<br>radii (10,<br>30 and 100<br>km);<br>Number of<br>fatal events</td><td>1 = same<br>as<br>Aspinall<br>et al.</td><td>1=<br>Number of<br>popul. per<br>distance<br>radii</td><td>1 = Log<br>number of<br>popul.</td><td>4= Popul.<br><30 and >30<br>km; visitors<br>number;<br>prior<br>fatalities;<br>prior<br>evacuations</td><td>1= Density<br>normalized by<br>the value of<br>the more<br>populated<br>municipality</td><td>1= Population<br>density within<br>5, 10, 30 and<br>100 km<br>distance radius<br>from the main<br>crater</td><td>8 tested=<br>V1, V2, V3,<br>V5, V6, V7,<br>V8, V9<br><br>Final= 6<br>based on<br>selected<br>PLR model:<br>V1, V2, V3,<br>V5, V6, V7</td></tr></tbody></table>
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