# Household resilience to slow onset flooding: A study of evacuation decision triggers in high-rise buildings along the Seine in Paris

Nathalie Pottier, Marc Vuillet, Nathalie Rabemalanto, Abla Mimi Edjossan-Sossou

► To cite this version:

Nathalie Pottier, Marc Vuillet, Nathalie Rabemalanto, Abla Mimi Edjossan-Sossou. Household resilience to slow onset flooding: A study of evacuation decision triggers in high-rise buildings along the Seine in Paris. International Journal of Disaster Risk Reduction, 2023, 95, pp.103858. 10.1016/j.ijdrr.2023.103858. hal-04165395

HAL Id: hal-04165395

https://hal.science/hal-04165395v1

Submitted on 16 Sep 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Household resilience to slow onset flooding: a study of evacuation decision triggers in high-rise buildings along the Seine in Paris.

Nathalie Pottier¹*, Marc Vuillet², Nathalie Rabemalanto,¹Abla Mimi Edjossan-Sossou²

Correspondence to: Nathalie Pottier (nathalie.pottier@uvsq.fr)

¹ nathalie.pottier@uvsq.fr, nath.rabe@gmail.com, UMI SOURCE, University of Paris-Saclay, UVSQ, IRD, France.

² marc.vuillet@eivp-paris.fr, medjossan@gmail.com_Paris School of Urban Engineering, Lab'Urba, University Gustave Eiffel, University of Creteil, France.

* Corresponding author: nathalie.pottier@uvsq.fr

## Abstract:

Are high-rise residents prepared to evacuate on their own in the event of a major flood that result in prolonged shutdown of utility networks?

If a slow-moving flood of the Seine affecting Paris and its suburbs is predicted, crisis management services plan for preventive power cuts, evacuation of high-rise buildings, and halting of public transportation. This article explores the triggers for household evacuations through a survey of 533 households in 11 high-rise buildings located along the Seine River in Paris. Three main factors were studied: the ability to evacuate oneself, to temporarily relocate and to reach this temporary relocation by one's own means. The triggers for the decision to evacuate, for 7 types of households interviewed according to their willingness and ability to evacuate, reveal that most respondents are partially dependent on public authorities due to the lack of housing alternatives. Those who can evacuate preemptively attach great importance to receiving warning messages and their accuracy in deciding whether to evacuate. Many are unfamiliar with the consequences of prolonged urban power outages on their daily lives and the need for preemptive evacuation.

These results show that more targeted information based on each household's profile could facilitate preventive evacuation. This would reduce people's dependence on the community and avoid cascading effects that could worsen crisis management. This pilot survey database is needed to calibrate agent-based simulation models that generate reliable and well-informed predictions for evacuation of high-rise buildings in the event of slow flooding and shutdown of urban service networks.

**Keywords:** large scale flood, preventive evacuation, high-rise building, household resilience, survey, triggers, Paris.
## 1. Introduction

39

40

41

42

43

44

45

46

A large-scale flood of the Seine River in the Paris basin would be a terrible challenge for crisis management services, residents, and the economic sector of France as a whole, whether or not the areas are directly affected by flooding. According to the OECD (2014; 2018), a flood with a water height comparable to the 1910 hundred-year flood would directly affect 1,000,000 people, for a flood duration of about one month. Nearly 2,000,000 customers would be without electricity and nearly 5,000,000 without water. A very large number of people would therefore be heavily impacted without suffering the direct effects of the flooding itself.

47

48

49

50

51

52

53

54

55

Various protection systems, including mobile or more traditional dikes, have been designed to limit the extent of flooding (OECD, 2014). Nevertheless, their effects appear to be highly uncertain, particularly due to the unknowns of the risk of rising water tables or the failure of a levee/dyke (Gache, 2014). As a result, many technical networks and urban services would be closed as a precautionary measure. The May-June 2016 flood resulted in the closure of the regional express train (RER C) in central Paris, which carries nearly 550,000 passengers per day, numerous power outages, and the evacuation of nearly 20,000 people. This flood, severe on several small tributaries of the Seine (Loing, Yvette, Essonne in particular), remained a phenomenon of low amplitude within the Ile de France region, being considered as a twenty-year flood in the city of Paris.

56

57

58

59

60

61

62

63

64

65

The risk of a large-scale flooding of the Seine would first raise the question of the fate of the 830,000 people living within the flood zone (OECD 2014), to which would be added the many people indirectly affected (power cuts, disruption of water supply and/or sanitation, etc.). People likely to have to be evacuated must be taken care of or be able to be rehoused for a period of several days, or even several weeks, anticipating the kinetics of the flooding Most importantly, there remains much uncertainty about potential flood zoning and the effects on network operations (Gache, 2014). Crisis management services fear that very few inhabitants will agree to self-evacuate as long as the effects of the flood remain little noticeable, and that suddenly, following the shutdown of certain services and/or unexpected water inflows (as was the case at the RERC or in the 16th arrondissement of Paris during the 2016 flood), a large number of people will suddenly have to be taken into care (Edjossan-Sossou et al., 2020).

66

67

68

69

70

71

72

73

The research presented in this paper investigates the ability of residents living in densely populated areas of Paris to self-evacuate and self-relocate in the event of a large-scale flooding of the Seine River resulting in preemptive power outages in their building. Kolen (2013) highlights the complexity of evacuation issues for large populations, stating that "as the size of an evacuation increases, so does its complexity." In this study, not only is the size of the population large in relation to the small area to be evacuated (see survey area overview below), but the height of the buildings in question exacerbates the complexity of the evacuation process. When would residents leave, given that the sense of safety in high-rise buildings may not support the decision to evacuate? Which household profiles are likely to leave
74

first? What factors facilitate or hinder household autonomy in the event of evacuation? These are just some of the questions raised by this case study.

75

76

77

Several researchers have studied the management of a large-scale flooding of the Seine River in the Ile de France region. These studies have examined the issue from a global perspective (Reghezza, 2006) and from a national and regional crisis management perspective (November & Créton-Cazanave, 2017), highlighting the overarching need for more thorough planning of population evacuation (Masson-Planchon & Reghezza-Zitt, 2017), in order to be able to better arbitrate the use of available resources to strike a balance between securing populations and the economy, via the operation of mobility in a degraded mode. Related work (Gueben-Venière and Sepot, 2017) has also raised major questions about the links between mobility reorganization and evacuations. Does preventing people from using the metro by closing stations and lines even before they are flooded have more acceptable consequences for people in the sense that a return to service could occur more quickly in the medium term? Or, on the other hand, would the flow of passengers transferred to the road network, already restricted by the closure of certain lines, have too great a socio-economic and security impact - the impossibility for the population to get to their place of work - to the point of jeopardizing the economic security of several sectors or types of businesses, such as SMEs and SMIs? In addition, if RATP (Autonomous Parisian Transport Company) buses are required to transport people to be evacuated, this will mean fewer buses available to allow people who can stay at home to move around. Other research focuses on the business continuity of operators of urban networks and services (Toubin et al., 2015; Bocquentin et al., 2018; 2020), the mobility and reassignment of employees who can no longer travel to their workplace (Lhomme et al., 2019), social impacts (Fujiki & Renard, 2018) and household evacuation factors (Fujiki, 2017; Edjossan-Sossou et al., 2020). Based on the cartographic exploitation of statistical indices and a bibliographic study, Fujiki (2017) adopted a global approach to estimate the number of households that would need to be relocated for several large-scale flood scenarios in the Parisian basin. This work is relevant for determining orders of magnitude for evacuation rates and evacuees requiring relocation. Several additional data could usefully refine and complete these results. These data relate to residents' perception of risk (Navarro et al., 2016), to precautionary gestures (Grothmann & Reusswig, 2006), and to the conditions related to self-evacuation and self-accommodation. Moreover, knowledge about flood zoning and network operators' business continuity plans are constantly evolving (new floods could lead to knowledge of new vulnerabilities).

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

100

101

102

103

104

105

The objective is to explore the triggers of self-evacuation behaviors and the rehousing capacities of residents of high-rise buildings that would be subjected to a scenario of large-scale flooding of the Seine leading to preventive power cuts. The information produced will be able to inform the Greater Paris crisis management services on the proportion of the population likely to remain under the responsibility of the public authorities. It will also be used to anticipate the chain of consequences of this situation during the crisis management phase (support for vulnerable people, food and logistical support for non-

106

107

108

109

110
111 evacuated residents, deferred evacuation procedures, etc.). In our study, the salient point is to convince
112 people to self-evacuate preventively without imminent danger to their life and without any danger to
113 their property located in heigh. Ideally, they should self-evacuate before preventive blackout and
114 before they see water in neighborhood streets. Moreover, these people increase their exposure to
115 other risks likely to cause domino effects which would amplify the disaster, such as the risk of fire and
116 the impossibility for firefighters to intervene quickly to rescue those who have remained at home. In this
117 case, slow kinetics flooding that does not cause death in the Paris region can turn into a deadly risk in
118 high-rise buildings that have not been emptied of their inhabitants. Evacuation is therefore critical in the
119 case of high-rise buildings to safeguard people's lives and their access to all basic services. Several
120 authors provide a clear explanation of what critical networks are and the different ways whereby they can
121 be interdependent. Using tangible examples, they show how network disruptions can significantly
122 worsen the crisis (Toubin et al., 2015; OECD, 2014). For all these reasons, preventive evacuation and
123 data collection on this topic must be encouraged. The Parisian crisis management services have also
124 identified self-evacuation in high-rise buildings of the "Front of Seine" as a priority. These investigative
125 data collecting could guide different crisis management scenarios. Data will be used to feed predictive
126 models of tower self-evacuation such as the one proposed by Edjossan-Sossou et al, (2020).
127

Thus, the paper asks the following questions:

128 • What are the triggers for the decision of households living in a high-rise building to evacuate?

129 • Do they depend on public transport or road accessibility to evacuate preventively, and do they have
130 the capacity to relocate temporarily (know where and how to get there)?

131 • Could active members of the household continue to access to their workplace after evacuation, or
132 could they continue to work remotely?

133

## 2. Literature review

135 Available literature on total evacuation in high-rise buildings is rare in France and rare elsewhere
136 except from fire evacuation concerns. Most of studies focus on emergency evacuation, evacuation speed
137 of occupants and modelling or fire evacuation preparedness or partial evacuation strategies (Ronchi E. &
138 Nilsson D., 2013; Akashah et al., 2020; Ding N. et al., 2021). However, total residential building
139 evacuation, self-evacuation, evacuation and slow kinetics flood, relocation approach for several weeks,
140 preventive blackout and their impacts in high-rise buildings are salient aspects poorly treated in the
141 academic literature (Ronchi E. & Nilsson D., 2014; Soltanzadeh A., et al., 2021; Fujiki, 2017). So, we
142 relied on the abundant literature review focusing on triggers of households' decision making to evacuate
143 when exposed to natural hazards. This will allow us to select the indicators that correspond to our
144 context.
145

145
Several authors, such as Cutter et al. (2003), propose global social resilience indicators, at the regional or metropolitan level (Cutter et al., 2014). This type of approach typically provides very interesting information to give useful orders of magnitude for crisis management planning. It enables the study of hazard zoning, exposure, and assessment of the overall social vulnerability of populations. As mentioned in the introduction, such an approach was implemented in the Paris metropolitan area (Fujiki & Renard, 2018).

The factors that lead households to decide whether to evacuate in the event of a hazard may be intrinsic or extrinsic in nature. These include, but are not limited to, factors related to a household's capacity, perception and awareness of risk, previous disaster experience, structural and functional habitability of the place of residence, social influence, and environmental factors facilitating or hindering the ability to evacuate or other factors such as warning messages, perception of protective and preventive measures, perception of stakeholders, and personal characteristics, e.g., people's physical condition (Mileti, 1995; Dash & Gladwin, 2007; Lim et al, 2016; Ahsan et al., 2016; Strahan et al, 2018a; Strahan et al, 2018b; Lechner et al, 2019).

There is evidence of correlations between the socio-demographic characteristics of households and their ability to leave or remain in a hazard-prone area (Parker et al., 2009). Depending on the specific context of the study area, sociodemographic characteristics that underlie a household's ability to evacuate may include, but are not limited to, gender (Mileti, 1995; Fraser et al., 2014; Luathep et al., 2013), household size (Luathep et al, 2013; Smith & McCarty, 2009), the presence of vulnerable individuals such as children, the elderly, or the disabled (Luathep et al., 2013; Lim et al., 2016), ownership of and access to a vehicle (Wright & Johnston, 2010; Luathep et al., 2013), access to an available relocation site (Chang et al., 2009), the presence of pets (Drabek, 2001; Heath et al., 2001a, Solis et al., 2010), etc. Because these factors vary across households and the extent of their influence also varies by context (Murray-Tuite & Wolshon, 2013), identifying households likely to evacuate can be complex. Various studies show that the effects of demographic variables are controversial (Baker, 1991; Huang et al., 2016). In addition to sociodemographic characteristics, intrinsic factors that may lead a household to evacuate include risk perception (Solis et al., 2009): people may make an appropriate evacuation decision if they are aware and understand their level of risk (Piatyszek & Karagiannis, 2012). According to Jumadi et al. (2018), risk perception can be understood as how households interpret the probability of the threat. Some households may consider themselves to be safe. As a result, they tend to think that an evacuation is not necessary. Several studies on wildfire evacuation intentions (McCaffrey et al., 2018; Toledo et al., 2018) offer quantification of the risk perception factor, as well as other specific factors: level of preparedness, in terms of vegetation management on the property, impact of evacuation on the risk of losing one's home, etc. As Rigos et al. (2019) note, if an evacuee is faced with repeated emergency scenarios that may be a false alarm (e.g., an evacuation drill, a false bomb threat, etc.) or a real threat, the effect of Aesop's wolf cry can severely affect their likelihood of evacuating (repeated false alarms decrease compliance). But the decision to leave or stay for a household does not necessarily
depend on their previous disaster experience. According to Dash and Gladwin (2007), experience and risk perception are not correlated explanatory factors but rather considered competitive in terms of the evacuation decision.

Decision-making of households exposed to natural disasters also depends on some extrinsic factors such as communication and information about the risk (De Jong & Helsloot, 2010; Lechner et al., 2019). Households may decide to evacuate if they hear appropriate emergency information. In addition, in the face of natural disasters, people may decide to leave due to the uninhabitability of their residence for safety, utility shutdown, and health reasons (Wright & Johnston, 2010). Thus, they may leave their homes when faced with a disruption in the lifelines provided by technical networks, including power outages, district heating shutdowns, or water supply failures (Chatterjee & Mozumder, 2015). As social beings, households' decision can be influenced by the society in which they live. They may decide based solely on their individual beliefs and abilities or follow the lead of their neighbors after seeing them evacuate (Lindell et al., 2005; Jumadi et al., 2018). Environmental cues can, for example, include hazard-related factors such as sights, sounds, or smells that indicate the onset of a disaster, or distance from the source of the hazard (Smith & McCarty, 2009; Lindell et al., 2015). This type of cue also relates to the "livability" of a household's neighborhood. Loss of the normal functioning of support systems and services (public transportation, businesses, etc.) necessary for a household's well-being and functioning can make it difficult to remain in the home (Wright & Johnston, 2010). Disruption to a household's daily routine is also a predictor of evacuation, especially if they have not adequately prepared their homes or themselves (Strahan et al., 2018a). Our study will focus on households' intrinsic and extrinsic factors to better understand their ability to self-evacuate, self-habitat, and move to a relocation place. This will allow for a typology of households based on their willingness and ability to evacuate. This typology could guide the design of evacuation strategies tailored to the local populations involved.

The international academic literature on evacuation preparedness and decision making for slow kinetic floods is scarce (Fujiki, 2017). Most studies address evacuation in fast-kinetic natural disasters: hurricanes (Huang et al. 2012; Dash & Gladwin, 2007), tsunamis, earthquakes, or wildfire (McLennan et al., 2019; McCaffrey et al., 2018). In these life-threatening contexts, the hazard characteristics were the driving factor in making the decision to evacuate (Whitehead et al., 2000; Whitehead, 2005; Huang et al., 2012).

Survey by researchers as research material, at least since the 1950s, have focused on people's responses to risk (Baker, 1991; Thompson et al., 2017), but most existing analyses of evacuation behavior focus on populations that have already experienced the situation (retrospective surveys). Some of the best-known studies include Baker, 1991; Dash & Gladwin, 2007; Dow & Cutter, 2000; Gladwin et al., 2001; Zaalberg et al., 2009. Some more recent papers have also used retrospective surveys, including Demuth et al., 2016; Lindell et al., 2019a; Wallace et al., 2016. There are relatively few papers on prospective surveys examining household intention to evacuate following a disaster compared to the abundance of post-disaster studies: this is the case in France, and it is also revealed in the article by
Huang et al. (2016) and Lindell et al. (2019a): only 11 studies based and reviewed on behavioral expectation studies compared to 38 identified on post-disaster evacuation studies.

222

Our study is based on a "prospective" survey. The prospective method is commonly used in medicine, management, psychology, etc. Nevertheless, evacuation modeling studies are also referred to as prospective studies (see for example Gladwin et al, 2007). They aim to predict what would happen based on a context and assumptions. Instead of using random parameters as in the modeling process, this paper relies on respondents' statements to provide an initial view of people's perceptions, capacities and willingness to evacuate through a qualitative method. Intention surveys are also rarer than disaster experience feedback surveys. It is indeed very complicated for a person to describe his behavior in a situation that he has never experienced before. It may therefore be interesting to compare the results of such a study with those of a similar area that suffered the impacts of a major slow-evolving flood. The key questions used in this study for the analysis of evacuation conditions were inspired by decision models from the academic literature. One of them is the Protective-Action Decision Model (PADM; Lindell & Perry, 1992, 2012), which summarizes very well the various factors influencing the psychological processes of evacuation decision-making. It analyzes environmental and social signals, the information process and its devices (source, access and preference to information channels, warning messages) and receiver characteristics (Huang et al., 2012). PADM also served as a theoretical model for studying self-evacuation patterns during the 2014-2015 Australian bushfires (Strahan et al., 2018b).

239

## 3. Materials and methods

### 3.1. *Model of households' typology according to preventive self-evacuation willingness and capacities*

Many studies examined determinants of households 'decision making using questionnaires and simulation based on decision support models (such as the PADM, Lindell M.K. et al., 2012). Our study also uses a survey. It can be considered as a "pilot survey" according to the subject of high-rise buildings evacuation in France (rare are high-rise residential buildings) crossed with historical large-scale floods in urban floodplains. A survey by paper questionnaire deposited in the mailbox of the high-rise building residents of the Front of Seine, was administered (detailed further). The questionnaire was built to identify intention behaviors and residents' profile evacuation willingness and capacity in the event of a large-scale flood of the Seine with preventive blackout in the whole towers on the "Front of Seine" slab.

Criteria that are well recognised in the literature as influencing such households' behavior were selected. Table_1 presents the five criteria selected from literature reviews used to build the typology architecture system presented in Fig_1. They were also selected because they can be assessed in the field by a survey.

254
255

256

<caption>Table 1</caption>

257

Criteria used to characterize self-evacuation households' typology

<table><thead><tr><td>Criteria</td><td>Coded value in Fig_1</td></tr></thead><tbody><tr><td>C1: intention to evacuate based on the stated reasons, bearing in mind that some people will not evacuate, regardless of those reasons (Fraser et al., 2013).</td><td>(1) the household is willing to evacuate and has indicated one or more reasons that might lead them to evacuate; (0) the household is not willing to evacuate</td></tr><tr><td>C2: feasibility of self-relocation (Chang et al., 2019)</td><td>(1) the household had one or more relocation place(s); (0) no relocation place</td></tr><tr><td>C3: ability to evacuate by their own means of transport (Luathep et al., 2013).</td><td>(1) the household would evacuate with their own vehicle; (0) the household does not know or would use another means to evacuate (public transport, a relative's car, transport provided by public authorities, other means thanks to solidarity)</td></tr><tr><td>C4: access to the workplace or the possibility of continuing to work remotely from their relocation place, as work obligations could reduce the likelihood of evacuation (Mesa-Arango et al., 2013)</td><td>(1) Active members of the household could continue to access to their workplace or work remotely; (0) they couldn't continue to access to their relocation place or work remotely</td></tr><tr><td>C5: the presence of vulnerable persons in the household (Lim et al., 2016)</td><td>(1) household with vulnerable persons; (0) household with vulnerable persons or other special consideration for evacuation</td></tr></tbody></table>

258

259

260

261

The criteria were structured to develop an architecture system of household typologies. Seven types of households were identified based on their willingness or unwillingness to evacuate and to their degree of evacuation autonomy (Fig. 1).

262

263

264

265

266

267

The architecture of the household typology system is composed of two typology levels. The first level **Erreur ! Source du renvoi introuvable** (Table_2) contains the 4 main types that can play a major role in effectiveness of a preventive evacuation according to two determinants: their willingness to evacuate (yes or no) and their ability to self-evacuate, which depends on their level of dependence (totally autonomous, partially dependent, totally dependent).

268

<caption>Table 2</caption>

269

Household typology according to evacuation capacities (first level)

<table><thead><tr><td>Type</td><td>Description</td></tr></thead><tbody><tr><td>Type 1 (T1)</td><td>Willing to evacuate and totally autonomous: all above criteria with the value "(C1=1)"</td></tr><tr><td>Type 2 (T2)</td><td>Willing to evacuate and partly dependent: declared one or more reasons that could push them to evacuate (C1=1) and at least one other criterion with the value "(C=0)"</td></tr><tr><td>Type 3(T3)</td><td>Willing to evacuate and totally dependent: declared one or many reasons that</td></tr></tbody></table>
<table><tr><td>Type 4 (T4)</td><td>could push them to evacuate (C1=1) and all other criteria with the value “(C=0)”<br/>Not willing to evacuate (C1=0).</td></tr></table>

270

The second typology level consists of splitting type 2 “partly dependent” (T2) into types “2a, 2b, 2c and 2d” according to the reason for dependency in the event of evacuation (Table_3).

273

<caption>Table 3</caption>

274

<table><caption>Household typology according to evacuation capacities (second level)</caption><thead><tr><td>Type</td><td>Description</td></tr></thead><tbody><tr><td>Type 2a (T2a)</td><td>declared one or more reasons that could lead them to evacuate (C1=1), partly dependent on the relocation place (C2=0), and/or partly dependent on public transport or other means of transport (C3=0)</td></tr><tr><td>Type 2b (T2b)</td><td>declared one or more reasons that could lead them to evacuate (C1=1) and partly dependent on whether they can continue to access their workplace or continue to work remotely (C4=0)</td></tr><tr><td>Type 2c (T2c)</td><td>declared one or more reasons that could lead them to evacuate (C1=1) and partly dependent on vulnerable people or other particularity that must be considered for evacuation (C5=0)</td></tr><tr><td>Type 2d (T2d)</td><td>declared one or more reasons that could lead them to evacuate (C1=1) and partly dependent on a combination of two criteria (C2=0 and/or C3=0 and/or C4=0 and/or C5=0), apart from the criteria combination of “having a relocation place” (C2=1) and would evacuate with their own vehicle (C3=1)</td></tr></tbody></table>

275

276

277

278

279

Statistical treatments of the survey results presented below aim to describe trends in evacuation intention of household's residential high-rise buildings. Multiples cross tabulations performed on each categorical data of households to deepen each of the seven household types.
<figure><img src="image_2.png" /><figcaption>Fig. 1. Typology of households according to their willingness and ability to evacuate</figcaption></figure>
283

## 3.2. Study area

285

The “Front of Seine” residential towers in the “Beaugrenelle” neighborhood in the 15th district of Paris was chosen as the study area (Fig.2).

Residents of the 15th Paris district benefit from a strategic position along the river with a breathtaking view of the Seine and the Eiffel Tower. This district is the riskiest and populated flood zone in the capital (INSEE, 2016) and has the tallest residential buildings in Paris. Density in the 15th district has been quite stable since 1968 at around 28,000 inhabitants/km² compared to 21,000 for Paris as a whole (INSEE, 2016).

293

The identity of the Front de Seine district is due to the existence of 20 high-rise buildings connected by an artificial pedestrian ground located two levels above the natural ground: “the slab”. On that slab, 14 of the 20 high-rise buildings are residential ones. Our sample covers 11 of the 14 residential high-rise buildings (Fig.3). The “Front of Seine” is home to 10,000 inhabitants and nearly 5,000 jobs. Each IGH represents on average the equivalent of a village of 850 people, with all the difficulties that this represents, especially in the event of evacuation. Economic interests in this district are also highly important. One of the biggest shopping malls in Paris is located here, Some of the high-rise buildings are house companies or short- and mid-term-stay hotel residences.

302

The high-rise building apartments of the Front of Seine are not built according to traditional street pattern, but according to the principle of vertical separation of functions (cf Fig_4). The first 4 levels are designed for road traffic ("Circuler"), and work/commerce spaces (“Travailler”). Housing (“Habiter”) is only starting from the 4-5th level. Most of the residential high-rise buildings are built on an area 1 Km long (0.62 miles) and 200 m wide (218 yds).

308

Although they are in the flood-affected area, households in high-rise buildings of the “Front of Seine” would not be flooded. Thanks to the slab with two levels above the natural ground, in the event of a hundred-year flood of the Seine like in 1910 in Paris, they would be dry islands in the middle of neighborhoods slowly but completely flooded districts.

Nevertheless, the river floodwater could slowly penetrate basements in buildings mainly by rising groundwater in the foundation walls. Consequently, the four levels of parking lots for residents will have to be evacuated even before apartments’ evacuation. This situation will generate a complex problem solving to notify the residents of the evacuation for their household and car if they have one. Another problematic point is the location of electrical systems and generators in the basement of buildings (either at level -2 or -1).

The actual issue for utility network operators in such a scenario is to implement preventive cuts of urban
service networks (electricity, gas, sewer network). This is a condition, in such a scenario, to limit damage
cost and facilitate post-flooding recovery. As the flood will not reach high-rise buildings residents'
apartment, they may think they will be safe staying at home. But it will affect them in their daily life
until it makes life impossible for them.

The questionnaire survey proposed below explore households' intention to evacuate
according to their knowledge and their perception of the risk and of the role of public
authorities.

327

<figure><img src="image_3.png" /></figure>

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

328

329

330

331

<caption>Fig.2. On the left, the residential high-rise buildings of the “Front of Seine” in the 15th district of Paris. On the right, the alignment in height of the buildings of Hausmann architecture (source: https://en.wikipedia.org/wiki/Front_de_Seine
https://fr.wikipedia.org/wiki/Transformations_de_Paris_sous_le_Second_Empire).</caption>
<figure><img src="image_5.png" /><figcaption>Fig. 3. Flood zone mapping in the 15th district of Paris (mapping by N. Rabemalanto, and N. Pottier according to the highest known waters of the Paris Flood Zone Prevention Plan, 2007).</figcaption></figure>

332

333

334

335
HABITER
(LIVE)

TRAVAILLER (WORK)

CIRCULER (TRAVEL)

<figure><img src="image_6.png" /><figcaption>Fig_4. Sectional view and pictures of the slab carrying the high-rise buildings of the “Front of Seine” in the Beaugrenelle district (sectional view: https://souslesjupesdelametropole.wordpress.com/2013/11/22/la-negociation-michel-holley-son-architecture-son-rapport-au-sol/; pictures: https://sophiecarles.art/serie/urbanisme-sur-dalle/).</figcaption></figure>

3.3. Questionnaire design

The purpose was to collect information on the self-evacuation decision of households exposed to a large-scale flood and a preventive black-out that could last for weeks in high-rise “Front of Seine” buildings. This investigation is part of the “RGC4” research project which aimed at determining how failures of urban services networks can affect crisis management services in a context of slow kinetics flood of the Seine. Data for this study was collected by means of a self-administered questionnaire based on the identified criteria influencing evacuation (Table_1) reported in the architecture model of Fig_1.
348

The questionnaire containing 23 questions was titled: "Are you prepared for the evacuation of the
Front de Seine towers?" (see Appendix).

349

350

Besides some basic information of the participants (head of household), such as their gender, age, occupation, number of household members, moving date and apartment floor (the higher it is the more it is trigger for evacuation), the 23 questions explored triggers for evacuation decision around three main items (variables): (1) the willingness to evacuate (agree or disagree), the reasons that make them decide to evacuate (two items, one with 10 proposals, the other one with 7 proposals about the most penalizing missing urban services for daily life), knowledge of risk and opinion concerning the action of public authorities and the latest French law on Civil Security; (2) the possibility to evacuate (depending on mobility, whether they own a car and where it is parked, pets easy to evacuate, no vulnerable people in the household, preparation time needed, ...) and for active ones, the possibility of continuing to work from the relocation place; (3) the knowledge of where to go if they receive an evacuation advice.

351

352

353

354

355

356

357

358

359

360

361

Below are some detailed variables of our questionnaire, also referenced in the broader literature as salient indicators of intended evacuation behaviors:

362

363

364

* Owning a pet and whether or not it is easy to transport : pets can hinder the evacuation process mainly because transporting them can delay or make departure more complex (Heath et al., 2001b).

365

366

* Vehicle ownership and level of parking: first, those with a car parked at level -2 or -1 are more likely to be forced to move it if necessary. Second, receiving an evacuation order for the parking lot might prompt them to prepare to evacuate quickly as well.

367

368

369

* Knowledge of some basic information and perceptions on the risk of flooding and the evacuation process (two items): this relationship is extensively treated in the literature (for example, Peretti-Watel, 2000; Becerra et al., 2013). In one item, each of the proposed was evaluated on the basis of True; False, Don't know. In the other item, the opinion on the Civil Security modernization law was rated on a Likert scale from Totally agree (=1) to Totally disagree (=4) and Without opinion (=5).

370

371

372

373

374

* Reasons to evacuate (two items): reasons must be distinguished from the departure timing, according to past findings (Huang et al., 2012; Lindell et al., 2005). The first item is composed of 10 proposals. The respondent ticks one or more answers among them. They include the influence of what others do. That's what some authors call the social cues (observations of other people's behavior that prompt them to evacuate; see Lindell et al. (2019a). They also include knowledge on the proportion of people who would be responsive to advice and evacuation orders from public authorities. The second item is composed of 7 proposals of urban services that would lead households to evacuate if they were missing more than three days, such as no more drinking water, toilets, elevator, food supplies, emergency services...). Respondents had to rank them in descending order of inconvenience for their daily life.

375

376

377

378

379

380

381

382

383
384 • The existence of a relocation place and the possibility of continuing going to work or working at
385 that place: the French Civil Security modernization law of 2004 recommends that people self-
386 evacuate and self-host and not rely solely on public authorities in the event of an evacuation. Here,
387 we test the hypothesis of individual household responsibility. We also test the degree of
388 understanding of the law. The resulting behaviors determine the number of households likely to
389 remain dependent of the community. The item asks respondents to specify the possible drop point,
390 which we will call "the relocation place". Having or not a relocation place depends on relationships
391 networks.

392 • Expectations regarding the evacuation process in terms of (1) actions to better prepare residents of
393 high-rise buildings for evacuation and (2) useful information to help households to leave on time.
394 Respondents could express themselves through open-ended questions.
395

396 **3.4. Data collection and difficulties in accessing highly protected buildings**

397 Those invited to participate in the survey were all the households living in each of the eleven high-
398 rise buildings of the “Front of Seine” where the survey was authorized (one apartment = one household
399 surveyed) (Table_4). Out of the 14 residential towers that stand on the slab of the “Front of Seine”, 11
400 were investigated thanks to the prior agreement of each responsible tower manager (president of the
401 syndic of co-ownership, social landlords or “president” of the tower). This agreement was necessary
402 because all these high-rise buildings were highly protected. Tower rules prohibit the distribution of
403 advertisements, no canvasser and even the postman can access residents' mailboxes. The tower guards
404 deliver mail to residents' mailboxes themselves in 9 of the 11 sample towers. As a matter of fact, except
405 for two of the eleven towers (Beaugrenelle and Keller in Table_4) which are made up of social housing,
406 other residential high-rise buildings of the “Front of Seine” are known to be luxury residences very well
407 guarded to prevent any entry of unauthorized persons. Each tower has a guard 24 hours a day (it is
408 generally the same guards who take turns). In several towers, many foreign residents, often businessmen,
409 live here for a few months a year.

410 Questionnaires were printed in French and in English, in a 4-pages double-sided glossy brochure.
411 With help of the guardians who had the agreement to let us access to mailboxes and leave a handful of
412 blank questionnaires on the reception desk, questionnaires were distributed in mailboxes and collected
413 over a 12-week period in spring and summer 2019 by a postdoctoral fellow, assisted by the project leader
414 researcher and occasionally by another postdoctoral fellow. Questionnaire surveys were self-
415 administered. Willing respondents could complete the survey at their convenience and return it in
416 postage-prepaid envelopes to the address of the project leader's research laboratory, or in the drop box
417 left on the reception desk of each building. The maximum response time was 2 months, corresponding to
418 the time between the start of the distribution and the closing of the survey. But each additional response
419 being valuable, late responses were accepted thanks to the postage-prepaid envelopes.
533 households returned valid questionnaires for a response rate of 23.3% (Table_4). Table_5 shows that our sample size is sufficient for correct estimates. With 2283 survey questionnaires distributed, the minimum sample size should be 329 (with 95% confidence level ; 5% margin of error). Even with the total sample population of 2510 and a confidence level of 99% with a 5% margin of error, our sample of 533 responses is higher than the minimum required of 527.

For good quality of the data set, it is still necessary that the sample is representative of the studied population. In the absence of statistical data available to estimate the representativeness of our sample, we asked the guardians of the towers, who know very well their residents, if the characteristics of the respondents obtained by survey (see Table_6) corresponded to the profile of their residents. In our case, they guaranteed us, overall, the representativeness of our sample. We can therefore consider that the dataset quality is satisfactory, according to the size of the sample compared to the total target population size, according to the “expert opinions” collected among the guardians of the towers.

Moreover, the response rate of 23.3% is within the range of responses rates from other households’ evacuation surveys: 25.7% for Hurricane Bret, 24.6% for Texas coastal evacuation expectations, 33.5% for Hurricane Katrina, and 35.6% for Hurricane Rita in post-disasters contexts (Huang et al., 2012 and 2016).

If we consider the difficulty encountered in accessing these highly protected buildings and their occupants, the survey period (many residents gone on a business or pleasure trip), and a topic about behavioral intentions for an event that hasn't happened for one century, this rate of 23.3% for what is called in France a “prospective” survey, is relatively like previous expectation US surveys (Lindell et al., 2019a).

<caption>Table 4</caption>

Characteristics of the eleven high-rise buildings “Front of Seine” surveyed and household sampling frames

<table><thead><tr><th>High-rise building surveyed</th><th>Age building</th><th>Nb of stages</th><th>Nb of apartments</th><th>Nb distributed</th><th>Nb of responses</th><th>% Obs. responses/total</th><th>% Nb Responses/Nb distributed</th></tr></thead><tbody><tr><td>Beaugrenelle</td><td>1979</td><td>30</td><td>192</td><td>190</td><td>47</td><td>8,8%</td><td><strong>24.73%</strong></td></tr><tr><td>Espace 2000</td><td>1976</td><td>31</td><td>202</td><td>195</td><td>42</td><td>7,9%</td><td><strong>21.53%</strong></td></tr><tr><td>Evasion 2000</td><td>1971</td><td>31</td><td>200</td><td>39</td><td>28</td><td>5,3%</td><td><strong>71.79%</strong></td></tr><tr><td>Keller</td><td>1970</td><td>29</td><td>432</td><td>471</td><td>151</td><td>28,3%</td><td><strong>32.05%</strong></td></tr><tr><td>Le Village Emeriau</td><td>1973</td><td>16</td><td>180</td><td>177</td><td>44</td><td>8,3%</td><td><strong>24.85%</strong></td></tr><tr><td>Mars</td><td>1974</td><td>31</td><td>200</td><td>220</td><td>39</td><td>7,3%</td><td><strong>17.72%</strong></td></tr><tr><td>Panorama</td><td>1974</td><td>30</td><td>200</td><td>214</td><td>37</td><td>6,9%</td><td><strong>17.28%</strong></td></tr><tr><td>Perspective 1</td><td>1973</td><td>30</td><td>214</td><td>225</td><td>24</td><td>4,5%</td><td><strong>10.66%</strong></td></tr><tr><td>Perspective 2</td><td>1975</td><td>30</td><td>234</td><td>180</td><td>25</td><td>4,7%</td><td><strong>13.88%</strong></td></tr><tr><td>Reflets</td><td>1977</td><td>33</td><td>256</td><td>172</td><td>42</td><td>7,9%</td><td><strong>24.41%</strong></td></tr><tr><td>Rive Gauche</td><td>1975</td><td>32</td><td>200</td><td>200</td><td>54</td><td>10,1%</td><td><strong>27.00%</strong></td></tr></tbody><tfoot><tr><td><strong>Total/Mean</strong></td><td><strong>1974</strong></td><td><strong>29</strong></td><td><strong>2510</strong></td><td><strong>2283</strong></td><td><strong>533</strong></td><td><strong>100%</strong></td><td><strong>23.34%</strong></td></tr><tr><td></td><td colspan="3"><strong>1970-79 (16-33)</strong></td><td></td><td></td><td></td><td></td></tr></tfoot></table>
446

Table_5

447

Minimum sample size required (https://fr.surveymonkey.com/mp/sample-size-calculator/)

<table><thead><tr><td>Target population size in the 11 high-rise buildings surveyed</td><td>Confidence level</td><td>Margin of error</td><td>Require Sample</td></tr></thead><tbody><tr><td>2283 distributed</td><td>95%</td><td>5%</td><td>329</td></tr><tr><td>2283 distributed</td><td>99%</td><td>5%</td><td>516</td></tr><tr><td>2510 total sample</td><td>95%</td><td>5%</td><td>334</td></tr><tr><td>2510 total sample</td><td>99%</td><td>5%</td><td>527</td></tr></tbody></table>

448

449

### 3.5. Data analysis

450

Survey data first entered on a Excel sheet was imported into the survey and data analysis software Sphinx IQ2 Quali (with automatic software update), to facilitate and deepen the first phase of analysis.

451

452

All statistical analysis used Sphinx IQ2 Quali and Sphinx DataViv tool (except from the two open-ended questions for which the word tree used an open-source online application: "www.treecloud.org").

453

454

Preliminary univariate analysis were conducted. Some questions had two questions in one, in order to answer easily for the respondents and shorten the questionnaire. These questions have been recoded or their responses grouped together to facilitate statistical analysis (see in Appendix, for example Q4 "have you got any pet(s)? with 3 possible responses "no", "yes, and easy to transport", "yest, and hard to transport" recoded "pet owenership" = "no" / "yes" (grouping 2 modalities). The descriptive analysis was performed using central tendencies mean, standard deviation, Pearson X² test, etc. to enhance the key variables understanding: socio-demographics, resident's information on the consequences of a large-scale flooding of the Seine, and their preference and behaviors regarding preventive evacuation decision, transport and temporary relocation. All the items have significance of p < 0.01 in the univariate analysis.

455

456

457

458

459

460

461

462

463

464

465

466

467

468

469

470

Lexical analysis illustrated by a tree cloud was performed on the two open-ended questions (Q17 and Q18, see questionnaire in Appendix) which asks participants to cite specific information and action they would need to better prepare for the evacuation and to be better informed. An ascending hierarchical classification was performed on the ten evacuation reasons (variables), in order to identify groups of households according to their willingness to evacuate. Finally, concerning triggers for evacuation, which combine the will and the ability to evacuate, secondary multivariate analysis were conducted and cross sorting analyses were used to foster deep understanding of the seven respondents' profile identified in Fig_1.

471

## 4. Results and discussion

472

### 4.1. Characteristics of survey respondents

473

The sample structure in Table_6 reflected the highly specific character of the high-rise buildings inhabitants of the "Front of Seine" in the 15th district of Paris. They were 229 men (43%) and 303 women (57%) with a high average age (83% over 45 years old, 48% over 65), households composed

474

475
476 mainly of one or two people (80.5%). These results are consistent with the trend for the whole of Paris
477 and of the 15th district, where 51% of the population lives alone (INSEE, 2019).

478 A small majority were retired or inactive residents (52.8%), moved in on average 20 years ago. 48.3%
479 lived above the 16th floor and 21.6% above the 24th floor. Few respondents had a pet (13%) and ¾ of
480 them could be easily evacuated; 52% owned a car (which corresponds to the high level of standing with
481 an assigned underground parking space, a privilege in Paris as in the other very large cities of the world).
482 In our sample, 61% lived without particularity of household members to be considered for evacuation.
483 Among those who lived in households with dependents (39%), 20% had dependent children, 44.7% had
484 elderly people, 10% had disabled people, almost 5% had members with regular medical or external
485 assistance. The household composition is decisive in the evacuation process because household members
486 tend to evacuate as a unit and households with dependent members are likely to have reduced and slowed
487 mobility.

488

489

<table><caption>Table_6. Characteristics of respondents (N = 533)</caption><thead><tr><th>Variable name</th><th>N</th><th>Range<br>Response frequency (N = number)</th><th>p-Value</th><th>Mean<br>(min-max)<br>Chi-Square X<sup>2</sup></th></tr></thead><tbody><tr><td>Gender</td><td>532</td><td>Male 43% (229)<br>Female 56.8% (303)</td><td>&lt; 0.01</td><td>X<sup>2</sup> 84 very<br>significant</td></tr><tr><td>Age</td><td>532</td><td>1= less than 25:1.1% (6)<br>2= 25-45: 15.6% (83)<br>3= 45-65: 35.5% (189)<br>4= greater than 65: 47.9% (255)</td><td>&lt; 0.01</td><td>3.33<br>X<sup>2</sup> 1255 very<br>significant</td></tr><tr><td>Number of household members</td><td>522</td><td>1 47.7% (254)<br>2 32.8% (175)<br>3 8.8% (47)<br>4 + 8.6% (46)<br>Total: 942 people</td><td>&lt; 0.01</td><td>1.8<br>(1-7)<br>X<sup>2</sup> 587 very<br>significant</td></tr><tr><td>Occupation</td><td>530</td><td>Active person 45.5% (241)<br>Retired person 48.1% (255)<br>Inactive (unemployed person) 4.7% (25)<br>Active and retired persons<br>1.7% (9)</td><td>&lt; 0.01</td><td>X<sup>2</sup> 404 very<br>significant</td></tr><tr><td>Particularity of household<br>members to be considered for<br>evacuation</td><td>532</td><td>No 60.9% (324)<br>Yes 39.1% (208)<br><br>Detail of “yes”<br>(100%, N= 208):<br>Disabled 10.1% (21)<br>Elderly people 44.7% (93)<br>Regular medical or external assistance<br>04.8% (10)<br>Children less than 15 years old 20.2% (42)<br>Another particularity 07.7% (16)<br>(pets, health problems, security)<br>Several particularities (among those mentioned)<br>04.9% (26)</td><td>&lt; 0.01</td><td>X<sup>2</sup> 587 very<br>significant</td></tr><tr><td>Floor</td><td>524</td><td>1= 0-8<sup>th</sup> 17.4% (91)<br>2= 9-16<sup>th</sup> 34.4% (180)<br>3= 17-24<sup>th</sup> 26.7% (140)<br>4= 25-33<sup>th</sup> 21.6% (113)</td><td>&lt; 0.01</td><td>2.52<br>(0-33)<br>X<sup>2</sup> 153 very<br>significant</td></tr></tbody></table>
<table><tr><td>Moving date</td><td>520</td><td>Before 1976: 09.4% (49)<br/>1976-1991: 23.8% (124)<br/>1992-2006: 27.9% (145)<br/>2007-2021: 38.8% (202)</td><td>&lt; 0.01</td><td>1999<br/>(1970-2019)<br/>X<sup>2</sup> 245 very<br/>significant</td></tr><tr><td>Vehicle ownership</td><td>532</td><td>No 48% (256)<br/>Yes 52% (276)<br/><br/>Detail of “yes”<br/>(100%, N= 276)<br/>Under the slab of buildings<br/>1<sup>st</sup> basement: 51.8% (143)<br/>2<sup>nd</sup> basement: 25.7% (71)<br/>1<sup>st</sup> level on the slab:<br/>2.5% (7)<br/>Elsewhere in the basement:<br/>2.5% (7)<br/>Elsewhere on the surface:<br/>17.4% (48)</td><td>&lt; 0.01</td><td>X<sup>2</sup> 264 very<br/>significant</td></tr></table>

<table><tr><td>Pet ownership</td><td>533</td><td>Yes 12.9% (69)<br/>No 87.1% (464)</td><td>&lt; 0.01</td><td>X<sup>2</sup> 1103 very<br/>significant</td></tr><tr><td>Animal evacuation (subsample<br/>“yes” owns a pet)</td><td>69</td><td>Easy evacuation 73.9% (51)<br/>Difficult evacuation 26.1% (18)</td><td>&lt; 0.01</td><td></td></tr></table>

## 4.2. Households knowledge on risk exposure and perception of preventive evacuation advices

Remember that our residents of the "Front of Seine" towers are questioned about a hazard and a context that they have never experienced: the sector has not experienced flooding for more than a century and the residential towers have never been without electricity for several weeks, or even several days.

Table_7 presents summary statistics for respondents' intentions on evacuation advisory, based on four questions submitted to them. Respondents were asked if they knew what to do in case of evacuation advisory. Only 18% said they know what to do, which indicates that they lack information as to the attitude to adopt in the event of a preventive evacuation notice (Q5). For vehicle ownerships, nearly 60% would not be ready to evacuate immediately (Q7, respectively 43% would stay at home and wait to better assess the dangerousness of the situation; and 16.6% don't know what decision they would make). This high rate reflects low risk awareness, certainly very low for human lives (slow flood), but detrimental for the crisis management services which must be able to count on the effective self-evacuation of able-bodied and autonomous people.

Respondents were also asked about their opinion on the law of Civil Security Modernization (Q9). This law, which date from 2004, is the heart of the French crisis management system. For what concerns us, it insists on individual responsibility as important as collective responsibility in evacuation decision making. In other words, it recommends that each able-bodied and autonomous person can "take charge of themselves", for example by self-evacuating and relocating on their own without waiting for the intervention of the public authorities. No clear opinion on the question despite a slight predominance of "agree" (52.7% totally agree or somewhat agree while almost 9% have no opinion). These results also show that public authorities would have to convince at least a half of the residents of the need for
preventive self-evacuation. This result is not surprising. In France and even more so in big cities and in Paris, people rely heavily on the support of public authorities to manage everything in the event of a disaster. But recent natural disasters around the world have allowed an awareness that is also gaining ground among Parisians.

Question Q10 in Table_7 complete these observations. 62.4% of respondents thought that the Prefecture (State service in the region responsible for crisis management and the dissemination of public information in the event of a crisis) would inform them one or two days at least before water invades their district. Indeed, this is the minimum time that should be left to inhabitants, according to the scenario of a hundred-year flood of the Seine. So, they know, and they think they will have time to anticipate the event, if an advisory is issued. But we will see later that they think that the public authorities will only issue an evacuation notice in the event of extreme necessity. However, preventive power cuts to reduce the cost of damage post-disaster will require issuing the evacuation notice even before the water invades the district. This may not be considered an "extreme emergency" by tower residents.

<table><caption>Table_7. Summary statistics for respondents' intentions on evacuation advisory (N = 533)</caption><thead><tr><th>Question number (see Appendix) and statement</th><th>N</th><th>Range (number = response frequency)</th><th>p-Value</th><th>Chi-Square X<sup>2</sup> Mean (min-max)</th></tr></thead><tbody><tr><td>Q5- Do you know what to do in case of evacuation advisory?</td><td>533</td><td>no (225=42,2%) ; in part (212=39,8%) ; yes (96=18%)</td><td>&lt; 0.01</td><td>X<sup>2</sup> 56.78 very significant</td></tr><tr><td>Q7- If you receive an evacuation advisory of parking's in the basements due to a flood, without advisory to evacuate the tower, what will you do? (question for car owners)</td><td>265</td><td>I stay at home, and I better assess how dangerous the situation is (114=43%); I prepare to leave the apartment within 24 hours (75= 28.3%); I take this opportunity to leave immediately (32= 12.1%); I don't know what decision I would make (44= 16.6%)</td><td>&lt; 0.01</td><td>X<sup>2</sup> 60.75 very significant</td></tr><tr><td>Q9- Except for fragile people, the French Civil Security modernization law recommends self-evacuation and self-hosting, but not only waiting for help from the public authorities. Do you agree?</td><td>533</td><td>Totally agree (115= 21.6%); Somewhat agree (166= 31.1%) Without opinion (46= 8.6%); Somewhat disagree (103= 19.3%); Totally disagree (103= 19.3%)</td><td>&lt; 0.01</td><td>X<sup>2</sup> 68.45 very significant</td></tr><tr><td>Q10- If the Prefecture issues an evacuation notice linked to a major flood of the Seine, do you think that it could ask you to evacuate:</td><td>518</td><td>Long before water invades your area (from 24 to 48 hours) (323= 62.4%) Only when the water has reached the streets and/or basements in this area (144=27.8%) Only of the flooding lasts too long (several days) (51= 9.8%)</td><td>&lt; 0.01</td><td>X<sup>2</sup> 453.6 very significant</td></tr></tbody></table>

In Fig_5, households' knowledge is examined using six statements about exposure to risk and the consequences of a blackout in their building. Households were asked whether they thought each proposed statement was "True" or "False" or if they "Don't know".

Responses revealed that most of the inhabitants of high-rise buildings on the "Front of Seine" seemed to have a correct idea of their risk exposure. They had also a good idea of the partial effectiveness of structural measures to protect the capital from flooding, and of the impossibility for the
534

535

public authorities to be able to evacuate and rehouse all the residents in the event of a large-scale flooding of the Seine.

536

537

538

539

55% are right to think that their area has already been flooded (a). Even if the oldest residents have lived in the district for more than 20 years, none had experienced the historic flood of 1910. However, this flood is quite well known among the population, and perhaps among Parisians because of the centenary which was “celebrated” in 2010.

540

541

542

543

The flood is a predictable phenomenon (b). The French public authorities are able to warn the population 72 hours in advance, i.e. 3 days. Therefore, they are not able to anticipate the magnitude of the flood a week in advance, as 42% of respondents believe. Responses showed the interest of emphasizing to residents the uncertainty in flood forecasting and the indicative nature of flood scenarios.

544

545

Most residents understood that structural measures (dams, etc.) were not 100% effective in protecting Paris from flooding (c). Only 7% though the opposite. 64% are sure and 29% do not know.

546

547

548

549

550

551

552

553

554

555

556

Most are also aware that the public authorities cannot rehouse all the inhabitants of the towers (f): only 9% though so. A few requisitioned gymnasiums would not be enough to rehouse temporarily the inhabitants of “the Front of Seine”, including the 2,510 households in the 11 surveyed towers. It is not to mention that if this district is evacuated, that means all the flood-prone areas of Paris and its suburbs will already be flooded with other inhabitants to be rehoused! One respondent claimed, “*The public authorities may objectively have the means to welcome everyone, but it may not be their priority, or they may have their own reason not to be willing to do so*”. Debating whether governments should welcome everyone is beyond the scope of this study. It raises a much broader and hotly debated question of the division of public and private responsibilities in such a situation (Godfrin et al., 2002). The dissemination of precise information on the interest of evacuating preventively takes on its full meaning regarding rehousing problems.

557

558

559

560

561

562

563

564

565

566

567

568

569

570

Fig_5 revealed above that between a third and a half of the respondents lack information to answer to the six statements. It is especially the case when they are asked on the consequences of an electrical blackout in the tower (d and e): 54% do not know if the tower has generators that guarantee an electrical autonomy of at least 4 or 5 days and 24% thought it is false. They are right. In France, the regulations for high-rise buildings require the use of 2 generators per tower. Each one must have a reserve of fuel to ensure 36 hours of functioning (Legifrance, 2012, art. HG43, § 1). Each building can therefore have an electrical autonomy of 3 days at most, insofar as the fuel reserves are full and checked regularly. The slab “Front of Seine” manager told us that most of the high-rise buildings are in the basements. They are therefore vulnerable to rising water tables. For example, in the “Keller” tower, the generator was moved from the basement to street level (less floodable). It took 12 years of negotiation between the city of Paris and the co-ownership trustee to release funds for the works. Therefore, emergency generators’ position and duration of autonomy are information to share with the residents of the towers to hope for a successful preventive evacuation. Graph e show that only 22% of respondents though they can keep water from the tap and sewage system running during a power cut. Most respondents know the main
571 inconveniences in the event of a power failure. Finally, responses in Fig_5 revealed that they had a rather

572 good perception of reality but above all lacked precise and practical information, a factor that hinders

573 preventive self-evacuation.

574
575

576

Fig_5

577

Respondents 'knowledge about the following statements (Q8 see Appendix)

578

N : 533 (total sample)

579

a- This area has never been flooded

<figure><img src="image_7.png" /></figure>

p-value = < 0,01 ; Chi2 = 131,6. Very significant

b- An exceptional flood of the Seine in Paris is predictable
at least a week in advance

<figure><img src="image_8.png" /></figure>

p-value = < 0,01 ; Chi2 = 23,8. Very significant

c- Thanks to all the infrastructure (dams, etc.), this area cannot be flooded at all

<figure><img src="image_9.png" /></figure>

p-value = < 0,01 ; Chi2 = 265,2 ; ddl = 2. Very significant

d- The tower has generators that guarantee an electrical autonomy of at least 4 or 5 days

<figure><img src="image_10.png" /></figure>

p-value = < 0,01 ; Chi2 = 106,9. Very significant

e- In the event of a power cut in the tower, I can keep water from the tap and the sewage system on

<figure><img src="image_11.png" /></figure>

p-value = < 0,01 ; Khi2 = 59,1 ; Very significant

f- Public authorities are able to house and/or rehouse all the inhabitants of the towers

<figure><img src="image_12.png" /></figure>

p-value = < 0,01 ; Khi2 = 146,4 ; Very significant

580

581

582

4.3. Triggers for households’ evacuation decision making in response to a preventive blackout due to
a large-scale flooding of the Seine

583

584

Analysis of prospective evacuation household behaviors focuses on triggers for households' evacuation
decision making (10 proposals) and self-evacuation capacity according to 3 dependent variables:
relocation place, transport to get there and continuity of work from the relocation place.

585

586

587

4.3.1. Analysis of reasons for households’ evacuation

588

Based on previous studies and on Edjossan-Sossou et al. (2020 and 2022), respondents were asked what reason would decide them to leave their tower for several weeks in the context of a major flood of the Seine (see Appendix, Q11). Fig_6 presents results of households ‘choices among 10 proposals.

589

590
Percentage of responses to the added 10 items represents 100% and each item is a part of the percentage (so a share of 25% in this representation represents 75% of respondents in a flat sorting by item). Respondents could tick several proposals, and this is what all the respondents did. This shows the complexity of the decision to evacuate which does not result from one but from several combined reasons. Our goal was to identify the main reasons by all those, that can influence households' decision. Respondents were asked a second question for ranking the first three reasons, among the 9 previously proposed (without the item "nothing, I will not leave in any case"), that would push them to evacuate for an indefinite period. A statistical treatment (contiguous analysis of each item) allowed us to represent the share of each of the 9 proposals in the ranking by order of importance of the reasons to evacuate: the first, the second and the third (Fig_7).

As we can see in Fig_6 and Fig_7, the three main reasons cited by the respondents to evacuate are:

* preventive evacuation advisory from the public authorities

* deterioration of living conditions

* knowing that the household could be hosted and being able to join that place with a means of transport,

The most important one is preventive evacuation advisory from the public authorities. Fig_6 shows that this reason represents a part of 22%, a high rate which indicates that almost three respondents on four checked this reason (flat sorting not shown here indicates that 71% of the 531 respondents checked this reason, and reduced in proportion to all the others, it is 22%). Fig_7 shows that this reason is ranked first by 48% of respondents, second by 12% and third by 8% of the respondents. This corroborates our previous results indicating that the inhabitants expect the authorities to take charge of the crisis. This result reflects the same findings as those obtained by Baker (1991), Dash & Gladwin (2007) and Kreibich et al. (2017), and the most rigorous and comprehensive review conducted by Huang et al. (2016) on the subject: official warnings are the most important predictor of evacuation decisions. Of course, several conditions, like the communication channel used and the clarity of the message, as reported by Baker (1991), Paul & Dutt (2010), Parker (2017) and Gissing et al. (2019) underpin this.

The two other main reasons (i.e. deterioration of living conditions and the existence of a private or a public relocation place with a means of transport to get there) have a greater direct impact on people than other reasons mentioned in the questionnaire such as seeing the neighbors leave, media information, etc.

As is commonly found, expected personal impacts strongly encourage people to protect themselves and better anticipate an evacuation (Fritzpatrick & Mileti, 1991; Huang et al., 2012; Lindell & Perry, 1992).

Information by the media, which could inform on the local deterioration of the situation is also a trigger not to be overlooked (part of 14% of the 10 proposals, almost as important for tower residents as the relocation place (Fig_6). It is cited by 42% of the respondents as one of the three reasons for evacuating (respectively 6% for the first reason, 21% for the second reason, 15% for the third reason (Fig_7)).
Alou (2018) observed that people sometimes have struggle to get the right information about a situation that would affect them directly. This situation often leads them to evacuate too late. Information through the media affects residents of high-rise buildings differently in comparison to residents of smaller buildings. Residents of the “Front of Seine” know that evacuation from their high-rise building should be a complex process. Building managers and managers of the basement car parks could play the role of local relay of information. They could be an efficient relay of local information to the resident’s towers.

635
636

637

638

If you had to leave your tower for several weeks because of a major flood of the Seine, what would decide you to do so? (Many possible responses)

<figure><img src="image_13.png" /><figcaption>Fig_6. Evacuation triggers among 10 proposals for households in high-rise buildings of the “Front of Seine”.</figcaption></figure>

639

640

641

Study population: total sample (N= 533); sample size: 531.

642

643

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

644

645

p-value < 0.01; Chi-scare 294; very significant.

b Preventive evacuation advisory from public authorities and emergency services (24-48h before this area gets flooded)

c Departure of at least half of my neighbors in my building

d Departure of at least half of my next-door neighbors

e Information from media or my sourrondings on the deterioration of the situation

f Knowing that if I don't leave on time, I could not count anymore on emergency service afterwards

g Knowing that my apartment will be in a secure area while I'm away

h Deterioration of living conditions

i Knowing where I could be hosted and being able to join that place j Other reason for evacuation

646
647 Fig. 7. Ranking of the first 3 reasons for evacuation among 10 proposals (contiguous analysis of each item).

648 Study population: total sample (N= 533); sample size: 531

649

650 4.3.2. *Typology of households according to self-evacuation reasons*

651 To go further in the analysis, an ascending hierarchical classification performed on the ten
652 evacuation reasons (variables) with the Sphinx iQ2 software (Fig_8). It highlights the groups of
653 explanatory reasons for the intention to evacuate, according to households' profiles.

654 The creation of a dendrogram based on the method of ascending hierarchical classification,
655 makes it possible to identify groups of respondents who have similar behavior profiles based on
656 the answers they gave to the question on the reasons for evacuation, in the event of a major
657 flood of the Seine (see Fig_8).

658 The dendrogram groups the respondents according to a resemblance criterion which is
659 expressed in the form of a matrix of distances (the horizontal lines). It expresses the distance
660 between each respondent taken 2 by 2. Two identical observations will have a zero distance.
661 The more dissimilar the 2 observations, the greater the distance between the lines.

662 Thus, the application of the CAH to our sample (N= 531) gives 4 distinct classes, ie 4
663 respondents 'profiles (A, B, C, D). They are themselves divisible into as many classes as there
664 are branches per major group: the numbers on the left of the dendrogram correspond to the
665 number of respondents per class or subgroup (according to the association of secondary factors
666 common to the group – here the reasons for the question numbers specified in red in the table
667 below the dendrogram). For more synthesis, our statistical classification has limited to 3 levels
668 of the dendrogram. For example, group A represents 185 people (35% of the sample) who can
669 be subdivided into 3 subgroups of 70, 47 and 68 people corresponding to the branches of the
670 dendrogram, according to the secondary factors.

671 The dendrogram makes it possible to identify 4 very distinct groups of residents according
672 to their willingness or not to evacuate. Table_8 summarizes characteristics of each group
673 resulting from hierarchical classification. The typology of the households living in the high-rise
674 buildings of the "Front of Seine" is divided into 4 groups: the "personal", "confident", the
675 "worried" and the "follower". This knowledge of the situation in which residents are likely to
676 evacuate or nor is valuable for the local authorities in charge of crisis management. Indeed, it
677 allows them to know on which levers to act and which means to mobilize and to anticipate by
678 favoring preventive evacuation and by reducing the chain of consequences that can quickly lead
679 to chaos.

680
<figure><img src="image_15.png" /></figure>

681

682

Discriminant power of the typology: 0.23

683

The correlation matrix (not shown here) indicates a standardized Cronbach's Alpha: -0.33. The low value of the alpha indicates that the independence condition of the explanatory variables is satisfied.

684

<table><thead><tr><th>Groups resulting from hierarchical classification (number of respondents) concerned</th><th>Factors influencing self-evacuation of residents and corresponding number of the question in the questionnaire survey, see Appendix. Characterization of the classes: For the variables in green (resp. red), the mean values of the class are significantly higher (resp. lower) than those of the rest of the sample.</th></tr></thead><tbody><tr><td>A (185) 35%</td><td>+ q11i_Be_hosted, q11h_deterioration_living_conditions, q11f_no_emergency_services, q11d Departure_next-door_neighbors - q11b_preventive_advisory, q11e_media_info, q11j_Other, q11c_neighbors Departure, q11a_no_evacuation</td></tr><tr><td>B (63) 12%</td><td>+ q11e_media_info, q11b_preventive_advisory - q11g_security, q11c_neighbors Departure, q11j_Other, q11a_no_evacuation, q11d Departure_next-door_neighbors, q11h_deterioration_living_conditions</td></tr><tr><td>C (126) 24%</td><td>+ q11g_security, q11j_Other, q11a_no_evacuation - q11i_Be_hosted, q11h_deterioration_living_conditions, q11e_media_info, q11f_no_emergency_services, q11d Departure_next-door_neighbors</td></tr><tr><td>D (157) 29%</td><td>+ q11e_media_info, q11h_deterioration_living_conditions, q11c_neighbors Departure, q11d Departure_next-door_neighbors - q11i_Be_hosted, q11g_security, q11j_Other, q11a_no_evacuation</td></tr></tbody></table>

685

<table><thead><tr><th>Class</th><th>Number</th><th>Percentage</th><th>Mean distance</th><th>Standard deviation</th><th>concentration</th></tr></thead><tbody><tr><td>A</td><td>185.00</td><td>34.84</td><td>1.17</td><td>0.23</td><td>0.27</td></tr><tr><td>B</td><td>63.00</td><td>11.86</td><td>0.80</td><td>0.23</td><td>0.19</td></tr><tr><td>C</td><td>126.00</td><td>23.73</td><td>1.18</td><td>0.26</td><td>0.27</td></tr><tr><td>D</td><td>157.00</td><td>29.57</td><td>1.16</td><td>0.29</td><td>0.27</td></tr><tr><td>Total</td><td>531</td><td>100.00</td><td>1.31</td><td>0.21</td><td>1.00</td></tr></tbody></table>

686

Fig_8 Dendrogram and corresponding table of respondent's groups classified according to the reasons mentioned favourable or not to self-evacuation.

687

688
689

690

<table><caption>Table_8 Groups of residents according to decision making factors for evacuation</caption><thead><tr><th>Groups resulting from hierarchical classification<br>(Percent of sample)</th><th>Decision making factors for evacuation</th></tr></thead><tbody><tr><td>A (35%)</td><td>The "personal", attaches great importance to its comfort (be hosted after the evacuation, fears deterioration of its living conditions or not being rescued, is concerned about the decision of its neighbors to mitigate)</td></tr><tr><td>B (12%)</td><td>The "confident", attaches great importance to the information transmitted to him. Both media and public authorities are influential in his decision</td></tr><tr><td>C (24%)</td><td>The "worried", attaches great importance to what can impact its personal safety and property, those who are not sure that their apartment will be safe are not ready to evacuate</td></tr><tr><td>D (29%)</td><td>The "follower", gives great importance to the decisions of their neighbors before deciding themselves to evacuate</td></tr></tbody></table>

691

692

693

694

695

### 4.3.3. Analysis of the ability of households to evacuate based on means of transportation and to the continuity of work for working people

696

697

698

699

700

701

702

703

704

In our case study, in the event of a large-scale flooding of the Seine, public transport (rail, metro) will be cut off first in the "Front of Seine" towers district where the underground network below the river level is very vulnerable to flooding. The occupants of the high-rise buildings who do not own a car would have to evacuate before the public transport stops. Others who have their car parked in basements should receive a basement evacuation notice before evacuating their apartments in the tower (but it is not certain that everyone knows it). Knowing residents' intentions to evacuate or not, it is also essential to know when, where and how residents plan to leave their neighborhood (as a reminder, in this paper, we do not deal with the issue of evacuation of the tower itself).

705

706

707

708

709

710

711

712

713

714

Table_9 shows detailed results of the three questions on these themes. 64% would know where to go in case of an evacuation order (in our case study, affluent population with one or more second homes or a network of friends), 36% do not know and rely on the public authorities (remainder to be paid by the community). A minority, but probably many more, doubt that they would be able to evacuate within 24 to 48 hours (in the case of an evacuation for at least a few weeks). Nearly 40% of respondents plan to evacuate by car (with their own car or that of their relatives who would come and get them). Public transportation and the subway would be the preferred means of evacuation. Fig_9 shows that even among car owners, a number would prefer another means of transport (19% would prefer public transport, 33% would prefer to be driven by their family or relatives for example). The continuity of public transport would also
condition the continuity of work for 57.5% of the people who answered "yes" to be able to continue to work from their place of temporary relocation. In the Parisian case, this is important information for the managers of the rail network and the metro. Their decisions will need to be coordinated with those of other utilities. The challenge is to keep public transportation operational as long as possible without risking damage to the system from water infiltration and flooding. The cost of damage would be very high and the return to service delayed for several months.

<table><caption>Table_9

Self-evacuation capacity according to 3 dependent variables: relocation place, transport to get there and continuity of work.</caption><thead><tr><th>Question number (see Appendix) and statement</th><th>N</th><th>Statement (number = response frequency)</th><th>p-Value</th><th>Chi-Square X<sup>2</sup> Mean (min-max)</th></tr></thead><tbody><tr><td>Q13- If you are given 24 to 48 hours to organize your evacuation before transport disruptions, where would you go?</td><td>501</td><td>On 100%:<br>I have got one or more places to go (321= 64%)<br>I have no place to go, it will depend on the accommodation provided by the authorities (180 = 36%)<br>Among them:<br>I don't know if I would leave in such a time (52= 10%), including 48 = 26% of those who don't have any place to go</td><td>&lt; 0.01</td><td>X<sup>2</sup> 305 very significant</td></tr><tr><td>Q14- How do you leave ?</td><td>531</td><td>By public transport (104= 19.5%)<br>By car 209= 39.5% (my car 160= 76.5%; relatives or friends come and take me 49=23.5%)<br>By transport made available by the public authorities, solidarity 63= 12%<br>I don't know 54= 10%<br>By public transport and/or by car 95= 18%<br>Other (walk, bike) 6= 1%</td><td>&lt; 0.01</td><td>X<sup>2</sup> 241 very significant</td></tr><tr><td>Q15- As regards to active people, do you think you would be able to continue working from where you would he hosted?</td><td>260</td><td>Yes (134= 51.5%)<br>If means of transport are available 77= 57.5% of "yes"<br>By teleworking 57= 42.5% of "yes"<br>No (71= 27.3%)<br>I don't know (55= 21.2%)</td><td>&lt; 0.15</td><td>X<sup>2</sup> 5.29 no significant</td></tr></tbody></table>

<figure><img src="image_16.png" /><figcaption>Cross sorting: How would you leave? / Vehicle ownership (0 = doesn't have a car; 1 = owns a car)</figcaption></figure>
728 p-value = < 0,01 ; Khi2 = 195,5. Very significant.

729 Study population: total sample (N= 533); sample size: 531.

730 Fig_9. Cross sorting: How would you leave? / Vehicle ownership.

731

732 4.3.4. Respondents' expectations regarding evacuation information and preparedness

733 Respondents were also given an opportunity to provide open-ended comments to add more context
734 and detail to their needs and expectations towards public authorities in the event of an evacuation notice.
735 These comments offered some useful insights to provide recommendations to improve the effectiveness
736 of a possible evacuation order in the high-rise building of the “Front of Seine”.

737 A word tree was generated from the text contained in the 521 responses to the open-ended question
738 17: “what would you like to be done so that you would be better prepared in case you need to leave?”
739 (Fig_10). This text is transformed into a visual tool where the words are arranged in a tree-like branching
740 structure which reveal recurrent ones and indicates the strength of their semantic proximity in the text.
741 For this, an open-source online application has been used :“www.treecloud.org”_(where the algorithms
742 were implemented by Gambette & Véronis, 2010). The figure which one obtains consists of branches of
743 words or "edges". These edges are all the longer as the word classes are the most significant (close to
744 each other, well separated from the rest on the figure). This visualization tip improves readability
745 compared to a simple word cloud. The advantage of the tree view is also to benefit from a better amount
746 of information (represented by several groups or “bags” linear nested words) and better quality of
747 information (considering global information by matching words in the tree). The coloring of the words
748 guides the reading according to different possible criteria.

749 Here in the treecloud, the font coloring associated with the words is linked to their frequency (from
750 light blue for the little cited word to red and bold for those cited several times). The respondents most
751 often cite the word “information”, which appears in red in the longer branch of the tree, upper right on
752 the figure. In this branch of words, the word “information” is associated in descending order with the
753 word “evacuation”, then “instruction” and “know”. In the symmetric branch (on the bottom left of the
754 figure), the words “informed”, “case”, “advance” are among the five words which have the highest
755 frequency; in addition to “flood” and “should”. Thus, the idea of being well informed, especially on the
756 practical modalities on “evacuation”, is the priority for the respondents who live in the high-rise
757 buildings of the “Front of Seine”.
758

758 Indeed, people very often ask to be informed of many details regarding the evacuation process. In
759 addition to information, people want clear instructions in a timely manner so that they can prepare. Some
760 indicated that receiving instructions at an early stage would help them prepare for relocation, which is
761 consistent with the widely accepted principle that timely warning allows for successful evacuation
762 decisions. Warning is an integral part of evacuation decision making.

763 Others responded that they would follow the information provided by the authorities. This echoes
764 our previous finding regarding the importance people place on evacuation instructions and advice given
765 by public authorities. Some respondents also emphasized the need for an evacuation drill, with some 766 even specifying the expected frequency of such a drill, such as once or twice a year. The issue of 767 communication is also addressed by respondents through the recurrence of the words "communication" 768 and "meetings". They would like to have regular meetings about the situation and to receive brochures 769 presenting the risks and safety measures. Providing this information can ensure that it is passed on to 770 their networks of acquaintances on the day the feared event occurs (Lindell et al., 2015).

<figure><img src="image_17.png" /><figcaption>Fig_10. Word tree of the respondents' expectations to be better prepared for an evacuation</figcaption></figure>

771

772

773

774

775

776

777

778

779

780

781

782

Much information about flood risk in the 15th District is already being shared at meetings, as well as in print and online (https://episeine.fr/, http://www.leparisien.fr/paris-75/83-300-habitants-du-xve-seraient-touches-par-une-crue-centennale-04-12-2016-6412278.php). It is likely that the perception of risk, which is less bad than expected, has been improved by these devices. Another way to keep people informed is to encourage "intermediary actors" (Filâtre et al., 2005) who would willingly receive, transfer, and translate information in real time between different categories of actors (Becerra et al., 2013). In the case of high-rise buildings, there are several possible intermediaries, including the building manager, the "tower president," or even a completely different person if necessary. In our survey, some respondents requested that the building manager be designated as an intermediary actor. This would help
783

recognize the strong bond of proximity to residents and civic responsibility in flood prevention. (Becerra *et al.*, 2013).

784

785

786

## 4.4. Final typology of households according to their willingness to evacuate and their degree of autonomy

787

788

Preventive evacuation can only be effective if households are ready to evacuate (willing to) and have the capacity to do so (physical and practical mobility).

789

790

In the previous section (3 Materials and Methods), we identified the types of households that crisis management services might be interested in, to better target their information to the target population and the interventions to be planned, in case of a preventive evacuation in the "Front of Seine" high-rise buildings. The seven types of households are summarized in Fig_1.

791

792

793

794

Fig_11 shows the percentage results for each household type. Almost all our representative sample of households living in the high-rise buildings on the "Front of Seine", 96%, would be ready to evacuate in the event of a major flood of the Seine leading to a preventive power cut in the towers and shutdown of utility networks.

795

796

797

798

<table><thead><tr><th colspan="3">First level of typology</th><th colspan="2">N = 533</th></tr><tr><th colspan="2">Willing to evacuate</th><th>T1+T2+T3</th><th>Number N=514 (96%)</th></tr></thead><tbody><tr><td>T1<br/>Totally autonomous</td><td>T1</td><td>Willing to evacuate and autonomous</td><td>31</td></tr><tr><td>T2<br/>Partly dependent</td><td>T2a<br/>T2b<br/>T2c<br/>T2d</td><td>Willing to evacuate but dependent on relocation and/or transportation to get there<br/>Willing to evacuate but unable to continue travel to workplace or working from the relocation site<br/>Willing to evacuate but dependent on limited physical ability<br/>Willing to evacuate but dependent for a combination of reasons</td><td>291<br/>65<br/>16<br/>37</td></tr><tr><td>T3<br/>Totally dependent</td><td>T3</td><td>Willing to evacuate and completely dependent (no relocation housing, no car, no relocation workplace, limited physical ability)</td><td>74</td></tr><tr><td>T4<br/>Not willing to evacuate</td><td>T4</td><td>Not Willing to evacuate</td><td>N=19 (4%)</td></tr></tbody><tfoot><tr><td colspan="4">p-value &lt; 0.01 Chi-square 745.24; very significant</td></tr></tfoot></table>

799

<caption>Fig_11. Results for households' typology based on willingness and ability to evacuate</caption>

800

801

77% are partially dependent, mainly because they do not have a place to stay and/or a car (T2a 55%). The issue of rehousing and transportation needs to be seriously addressed by governments.

802

803

Moreover, knowing where to go in case of evacuation is one of the three main reasons given for the
804

805

decision to evacuate. In our very particular case, where only 1/3 of the households surveyed do not have a predefined place to go, they can rely on the public authorities to answer this question.

806

807

808

809

810

811

812

Therefore, public authorities may have to anticipate a double phenomenon in the event of an evacuation: (i) the first level of typology reveals a very small number of people who do not wish to evacuate, but many others may also not evacuate if they do not know where to go; and (ii) for those who are willing to evacuate, most of them rely on the assistance of public authorities. Type T2b (12%) confirms that relocation location and mobility are key issues. The stakes are high because these parameters would have a direct impact on maintaining economic activity in the capital region during flooding.

813

814

815

816

817

818

Type T2b (12%) confirms that relocation location and mobility are key issues. The stakes are high because these parameters would have a direct impact on maintaining economic activity in the capital region during flooding. Breaking T2 down into subgroups helps to understand why the debate on the Civil Security Modernization Act is so sensitive and often controversial. It advocates individual responsibility and implies self-evacuation and self-housing, but this last point is precisely the brake for 67% of households ready to evacuate (T2a+T2b).

819

820

Totally dependent individuals (T3, 14%) are likely to be socially isolated. They are dependents for the community in case of evacuation.

821

822

823

824

825

826

827

828

829

Group T4 “not willing to evacuate”, 4%, does not differ from the others based on socio-economic characteristics (cross-tabulations with the descriptive data in Table_6 do not indicate anything significant). According to the significant results of the cross-sorting with the risk knowledge and opinion questions (Q8, Q9, Q12, Q16, see Appendix), only the absence of an elevator for more than 3 days is determined to evacuate this group is most represented above the 17th floor. Previous US studies have shown that the variables that predict behavioral expectations also predict actual disaster response (Huang et al., 2016). This small proportion of T4 could therefore be misleading. In a real-world context of flooding and evacuation advice, crisis management services generally expect that a larger proportion of people would not be willing to evacuate.

830

831

832

833

834

835

836

837

A comparison between the 7 types of households was carried out, based on a multiple cross-sorting with socio-demographics and knowledge and opinion variables (age, gender, floor, number of household members, pets, and Q8, Q9, Q12, Q16, see Appendix). Results do not indicate supplementary differences in the socio-demographics variables (unsurprisingly the most autonomous are the youngest and those who live on the highest floors). The only notable result is that the T1 totally autonomous group attaches more importance to the safety of its apartment in the event of evacuation than the others and that the partially dependent groups T2b and T2d attach more importance to the public authorities and the guidance that they could take from them.

838

839

840

The great revelation of this typology is that almost all households are ready to evacuate in case of need, most households living in these towers do not have strong physical constraints of mobility. But they rely for the most part on public authorities and are very dependent on public transport, even if they
are more numerous than Parisians to have a car. Moreover, the car is not a criterion of autonomy since a part of households does not intend to take it to leave.

In total, four factors were found to be important to the respondents and could encourage them to evacuate: (1) evacuation advice from public authorities; (2) being able to relocate (implied other than in shelters or gyms) and being able to get there; (3) the disconnection of essential water and sanitation services in their building; 4) clear and formal information about the hazard and its consequences. The different actors (public authorities, crisis managers and managers of public transport, responsible for towers) must better know their populations and consider these "triggers" to anticipate evacuation behavior in the "Front of Seine" towers.

## 5. Conclusion

This paper presents the results of a study conducted on the triggers of self-evacuation behaviors of high-rise building residents who would be subjected to a large-scale slow flooding scenario requiring preemptive power outages in their towers. Three main factors were studied based on a field survey in eleven high-rise residential buildings on the banks of the Seine in Paris: the willingness and ability of households to evacuate, to relocate temporarily, to get to this temporary relocation by their own means (and for those in employment, the ability to get to their place of work or to continue to work by teleworking).

Although the results revealed that very few households would refuse to evacuate a priori (4%), the main results of our typology revealed that most respondents would be partially dependent in case of evacuation. The evacuation would be complicated by the ignorance of the consequences of a prolonged power cut for their daily life in a high-rise building where evacuation cannot be done from the outside, the dependence of nearly 2/3 to public transport (rail network, metro which will be stopped first), if we also count those who have a car but do not intend to leave with it (elderly people, alone, perhaps a Parisian specificity); and finally, by the problem of rehousing for several weeks.

It was a first challenge to collect data on this subject which has never been addressed in France and has also been neglected in the foreign academic literature. This "pilot" survey revealed the importance of residents' knowledge (local, targeted, and accurate) and awareness as a trigger for evacuation decisions, both vertical (another topic we have not addressed here) and horizontal evacuation (our topic), as Mileti & Beck, 1975, Ronchi E. & Nilsson D., 2014; Lennan et al., (2019), Akashah et al., (2020); and others have noted in previous studies. Information should focus on the consequences of non-evacuation or delayed evacuation for the daily lives of residents and their safety, rather than just the incentive to evacuate. This should reduce the proportion of those who delay their evacuation to the point of compromising their safety and calling in help when they could have easily anticipated. This study underlines too the need to incorporate representations of behavioral aspects of residents based on field
877 local surveys in predictive multi-agent simulation models (Edjossan-Sossou et al, 2020). This will
878 improve crisis management scenarios closer to reality.

879 Future research and model development should focus on studying evacuation preparedness actions for
880 high-rise buildings located in slow and sustained flood zones, the behavior of residents subject to
881 prolonged power outages and group dynamics, and relocation and business continuity strategies in large
882 cities subject to prolonged flooding.

883
<figure><img src="image_18.png" /></figure>
<figure><img src="image_19.png" /></figure>
9. Except for fragile people, the modernisation law of the Civil Security recommends the self-evacuation and self-hosting BUT NOT JUST EXPECTING the help from public authorities. Do you agree?

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

I totally agree

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

I totally disagree

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

I would rather agree

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

No opinion

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

I do not really agree

Specify your opinion:

10. If the prefecture issue an evacuation advisory linked with a major flood of the Seine, do you think they might ask you to evacuate:

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

Long before water invades your area (from 24 to 48h)

<figure><img src="image_26.png" /></figure>

Only when the water has reached the cellar and/or the streets in this area

<figure><img src="image_27.png" /></figure>

Only if the flood in this area lasts too long (several days)

11. If you had to leave this tower for several weeks due to a major flooding of the Seine, what would incite you to make that decision? (Many possible options)

<figure><img src="image_28.png" /></figure>

a) Nothing, I will not leave my home in any case

<figure><img src="image_29.png" /></figure>

b) The preventive evacuation advisories from the public authorities and the emergency services (24-48h before this area gets flooded)

<figure><img src="image_30.png" /></figure>

c) The departure of at least half of my neighbours in my building

<figure><img src="image_31.png" /></figure>

d) The departure of at least half of next-door neighbours

<figure><img src="image_32.png" /></figure>

e) The information from the media or my surroundings about the degradation of the situation

<figure><img src="image_33.png" /></figure>

f) Knowing that if I do not leave on time, I could not count anymore on the emergency services afterwards

<figure><img src="image_34.png" /></figure>

g) Knowing that my apartment will be in a secure area

<figure><img src="image_35.png" /></figure>

h) The deterioration of living conditions (at my place and/or in this area)

<figure><img src="image_36.png" /></figure>

i) Knowing where I could be hosted and being able to join that place

<figure><img src="image_37.png" /></figure>

j) Other reason (to be specified):

12. Among these reasons above (from b to j), which are the 3 first reasons which would incite you to leave?

1st:

2nd:

3rd:

13. If you are given 24 to 48h to organise your evacuation before the disturbance of the transportations, where would you go? (this detail is critical for the mobility plan outside the disaster area)

<figure><img src="image_38.png" /></figure>

I have got one or many possible places to go

<figure><img src="image_39.png" /></figure>

I have no place to go, it will depend on the housing provided by the authorities

Specify your eventual host city(/ies) or place(s):

<figure><img src="image_40.png" /></figure>

I do not know if I would leave within such a time limit

14. How would you leave?

<figure><img src="image_41.png" /></figure>

By public transport

<figure><img src="image_42.png" /></figure>

I count on the means of transport from the public authorities, the solidarity...

<figure><img src="image_43.png" /></figure>

By car

<figure><img src="image_44.png" /></figure>

I do not know

<figure><img src="image_45.png" /></figure>

My relatives or friends, who will host me, would probably come and take me by car

<figure><img src="image_46.png" /></figure>

Other (to be specified):
<figure><img src="image_47.png" /></figure>
Authors contributions. This work was carried out by Nathalie Pottier (NP) as part of the “RGC4” project coordinated by Marc Vuillet (MV). Nathalie Rabemalanto (NB) assisted, as post-doctoral researcher, in this investigation under the direction of NP. NB conducted the survey. She was assisted by NP and Abla Mimi Edjossan-Sossou (AMES) for distribution and collection, and by AMES for treatments and on technical points. The first draft of the paper and the methodology for identifying households evacuation typology (Fig_1) was carried out by NR. AMES, MV and NP contributed to the concept and writing. For the final version, NP has completed entering survey data into Excel, processing with Sphinx IQ2 and DataViv, and re-edition. MV helped with final edition, revisions as well as proofreading.

Competing interests. The authors declare that they have no conflict of interest.

Acknowledgements. This work was developed within the framework of the research project “RGC4: “Urban resilience and crisis management in a slow kinetics flood context. Development of tools to help manage critical technical networks: application to Grand Paris”, of the French National Research Agency. We would like to express our sincere thanks to the public partners who facilitate the survey (the Municipality of the 15th district, the City of Paris and the Paris Prefecture), to all our contacts like the presidents of co-owners associations, co-ownership trustees, tenant associations (especially the association Keller village, her president and her assistant), and the managers of high rise buildings, without whom the survey would not have been possible, and finally, of course, the resident households who responded to our survey.

Financial support: This research has been supported and funded by the French National Research Agency (ANR15 CE39 0015).

911
1. Ahsan, N. S.; Takeuchi, K.; Vink, K. & Ohara, M. A systematic review of the factors affecting the cyclone evacuation decision process in Bangladesh. *J. Disaster Res.*, **2016**, *Volume 11, Issue 4*, pp. 742-753. https://doi.org/10.20965/jdr.2016.p0742

2. Akashah F. W., Baaki T. K., Anuar M. F., Azmi N. F., Yahya Z. Factors affecting adoption of emergency evacuation strategies in high-rise office buildings. *J. of Design and Building Environment, vol 20(3) 1-21.* (2020). https://doi.org/10.22452/jdbe.vol20no3.1

3. Alou, A. A. La ville de Niamey face aux inondations fluviales. Vulnérabilité et résilience des modes d'adaptation individuels et collectifs. **2018**, PhD Thesis, Université Grenoble Alpes, France, 153 p. Available online at https://tel.archives-ouvertes.fr/tel-01945249/document.

4. Baker, E. J. Hurricane evacuation behavior. *Int. J. Mass Emerg. Disasters*, **1991**, *Volume 9, Issue 2*, pp. 287-310.

5. Becerra, S., Peltier, A.; Antoine, J.-M.; Labat, D.; Chorda, J.; Ribolzi, O.; Daupras, F. & Dartus, D. Comprendre les comportements face à un risque modéré d'inondation. Étude de cas dans le périurbain toulousain (Sud-Ouest de la France). *Hydrolog. Sci. J.*, **2013**, *Volume 58, Issue 5*, pp. 945-965. DOI 10.1080/02626667.2013.786181.

6. Bocquentin M., Vuillet M., Lhomme S., Cariolet J-M., Pottier N., Diab Y. "'étude des (inter) dépendances et des défaillances en cascade au sein des réseaux urbains : le cas d'une crue de la Seine en Ile de France" De la prévision des crues à la gestion de crise, *Colloque SHF*, **2018**, Avignon, 14&15 novembre 2018.

7. Bocquentin M., Vuillet M., Cariolet J-M., Lhomme S., Diab Y. Vers une meilleure prise en compte des défaillances en cascade au sein des réseaux franciliens interdépendants face aux crues *Revue La Houille Blanche*, **2020**, n°1:70-78 doi.org/10.1051/lhb/2020009.

8. Chang, S. E.; Pasion, C.; Yavari, S. & Elwood, K. Social impacts of lifeline losses: Modeling displaced population and health care functionality. In: Tang, A. & Werner, S. (Eds.), Proceedings of 2009 Technical Council on Lifeline Earthquake Engineering (TCLEE) Conference, Oakland, USA, June 22 – July 1, 2009, pp 563-572.

9. Chatterjee, C. & Mozumder, P. Hurricane Wilma, utility disruption, and household wellbeing. *Int. J. Disast. Risk Re.*, **2015**, *Volume 14, Part 4*, pp. 395-402. DOI 10.1016/j.ijdrr.2015.09.005.

10. Cutter, S. L., B. J. Boruff and L. W Shirley, Social Vulnerability to Environmental Hazards, Social Science Quarterly, **2003**, vol. 84, n ° 1, pp. 242-261. DOI: 10.1111 / 1540-6237.8402002.

11. Cutter, S.L., R. Schumann, and C.T. Emrich. "Exposure, Social Vulnerability and Recovery Disparities in New Jersey after Hurricane Sandy", Journal of Extreme Events, **2014**. 1(1): 23 p.
946

947

948

12. Dash, N. & Gladwin, H. Evacuation decision making and behavioral responses: Individual and household. *Nat. Hazards Rev.*, **2007**, *Volume 8, Issue 3*, pp. 69-77. DOI 10.1061/(ASCE)1527-6988(2007)8:3(69).

949

950

951

13. De Jong, M. & Helsloot, I. The effects of information and evacuation plans on civilian response during the national Dutch flooding exercise "Waterproef". *Procedia Eng.*, **2010**, *Volume 3*, pp. 153-162. DOI 10.1016/j.proeng.2010.07.015.

952

953

954

14. Demuth, J. L.; Morss, R. E.; Lazo, J. K. & Trumbo, C. The effects of past hurricane experiences on evacuation intentions through risk perception and efficacy beliefs: A mediation analysis. *Weather Clim. Soc.*, **2016**, *Volume 8, Issue 4*, pp. 327-344. DOI 10.1175/WCAS-D-15-0074.1.

955

956

15. Ding N, Chen T, Zhu Y, Lu Y. State-of-the-art high-rise buildings emergency evacuation. *Physica A*, **(2021)**, 561, 25168. https://doi.org/10.1016/j.physa.2020.125168

957

958

959

16. Direction de l'Urbanisme, du Logement et de l'Équipement (DULE). Plan de prévention des risques d'inondation du département de Paris révisé - Rapport de présentation de la révision. **2007**, 32 p. Available online at http://sigr.iau-idf.fr/amfphp/services/visiaurif_risques/aides/pdf/ppr/ppri/presentation_7500.pdf.

960

961

962

963

17. Dow, K. & Cutter, S. L. Public orders and personal opinions: Household strategies for hurricane risk assessment. *Global Environmental Change Part B: Environmental Hazards*, **2000**, *Volume 2, Issue 4*, pp. 143-155. DOI 10.3763/ehaz.2000.0220.

964

965

18. Drabek, T. E. Disaster warning and evacuation responses by private business employees. *Disasters*, **2001**, *Volume 25, Issue 1*, pp. 76-94. DOI 10.1111/1467-7717.00163.

966

967

968

969

19. Edjossan-Sossou A.M., Vuillet M., Mehdizadeh R., Deck O. Agent-based model for simulating households' self-evacuation decision in high-rise buildings under critical infrastructures failures induced by a slow-onset flood conditions – a case study in Paris. *IDRIM Journal*, **2020**, *10 (2)*: 35-65. ISSN: 2185-8322. DOI:10.5595/001c.18160.

970

971

972

973

974

20. Edjossan-Sossou A-M., Hajjar A., Rabemalanto N., Chevalier A., Pidoux J., Pottier N., Deck O., Vuillet M (2022) Modélisation de la décision d'auto-évacuation des ménages face à une défaillance du réseau électrique en situation de crise à partir d'une étude empirique des inondations de 2016 et 2018 en Ile-de-France" *23e Congrès de Maîtrises des Risques et Sûreté de Fonctionnement*, EDF Lab Paris Saclay, 10-13 octobre 2022.

975

976

21. Filâtre, D.; de Terssac, G.; Albanel, X.; Catlla, M. & Volery, I. Les dynamiques intermédiaires au cœur de l'action publique. **2005**, Octarès Editions, 320 p.

977

978

22. Fitzpatrick, C. & Mileti, D. S. Motivating public evacuation. *Int. J. Mass Emerg. Disasters*, **1991**, *Volume 9, Issue 2*, pp. 137-152.

979

980

981

23. Fraser, S. A.; Leonard, G. S. & Johnston, D. M. Intended evacuation behavior in a local earthquake and tsunami at Napier, New Zealand. **2013**, GNS Science Report 2013/26, 55 p. Available online at https://www.gns.cri.nz/static/pubs/2013/SR%202013-026.pdf.
982

983

984

24. Fraser, S. A.; Wood, N. J.; Johnston, D. M.; Leonard, G. S.; Greening P. D. & Rossetto, T. Variable population exposure and distributed travel speeds in least-cost tsunami evacuation modelling. *Nat. Hazards Earth Syst. Sci.*, **2014**, *Volume 14*, pp. 2975-2991. DOI 10.5194/nhess-14-2975-2014.

985

986

987

25. Fujiki, K. & Renard, F. A geographic analysis of post-disaster social impacts on a municipal scale – A case study of a potential large scale flood in the Paris region. *Geographia Technica*, **2018**, *Volume 13, Issue 2*, pp. 31-51. DOI 10.21163/GT_2018.132.03.

988

989

990

991

992

26. Fujiki, K. Etude prospective des impacts sociaux d'une inundation majeure en région Ile-de-France. Disparités socio-spatiales dans la prise en charge des populations franciliennes en situation de crise et post-crise : Une analyse cartographiée et quantifiée des besoins des ménages, de l'évacuation à la reconstruction. **2017**, PhD Thesis, Université Jean Moulin Lyon 3, France, 485 p. Available online at https://tel.archives-ouvertes.fr/tel-01760843/document.

993

994

27. Gache, F. Impacts envisageables d'une crue majeure de la Seine dans l'agglomération francilienne sur les droits de l'homme. **2014**, In : Désastres et Droits Fondamentaux. CADHOM, Paris.

995

996

28. Gambette, P. & Véronis, J. Visualising a text with a tree cloud. In: Locarek-Junge, H. & Weihs, C. (Eds.), Classification as a Tool for Research, **2010**, Springer Berlin Heidelberg, pp 561-569.

997

998

999

29. Gissing, A.; O'Brien, J.; Hussein, S.; Evans, J. & Mortlock, T. Townsville 2019 flood: Insights from the field. Bushfire and Natural Hazards CRC N° 468.2019, Melbourne. **2019**, 13 p. Available online at https://www.bnhcrc.com.au/sites/default/files/managed/downloads/townsville_2019_flood_insights_from_the_field_2.pdf.

1000

1001

30. Gladwin, C.; Gladwin, H. & Peacock, W. G. Modeling hurricane evacuation decisions with ethnographic methods. *Int. J. Mass Emerg. Disasters*, **2001**, *Volume 19, Issue 2*, pp. 117-143.

1002

1003

31. Godfrin, V. ; Merigot, M. ; Verdier-Chouchane, A. ; Lalo-Amenc, A. & Glatron, S. Impact de l'information préventive sur l'évolution de la responsabilité dans le cadre des risques naturels majeurs. Rapport de recherche pour le Programme Evaluation et prise en compte des risques naturels et technologiques. **2002**, 245 p. Available online at http://bfw.ac.at/crue_documents/pjr_371_117.pdf.

1004

1005

1006

1007

1008

32. Grothmann, T. & Reusswig, F. People at risk of flooding: Why some residents take precautionary action while others do not. *Nat. Hazards*, **2006**, *Volume 38, Issues 1-2*, pp. 101–120. DOI 10.1007/s11069-005-8604-6.

1009

1010

1011

33. Gueben-Venière S., Sépot F. 2017 Comment l'exercice Sequana peut-il être utile pour se former à gérer la complexité des effets dominos ? **2017**, sous la direction de November, V. & Créton-Cazanave, L., La Documentation Française, Paris, ISBN :978-2-11-010177-8, 237 p.

1012

1013

1014

34. Heath, S. E.; Beck, A. M.; Kass, P. H. & Glickman, L. T. Risk factors for pet evacuation failure after a slow-onset disaster. *J. Am. Vet. Med. A.*, **2001a**, *Volume 218, Issue 12*, pp. 1905-1910. DOI 10.2460/javma.2001.218.1905.

1015

1016

1017
35. Heath, S. E.; Kass, P. H.; Beck, A. M. & Glickman, L. T. Human and pet-related risk factors for household evacuation failure during a natural disaster. *Am. J. Epidemiol.*, **2001b**, *Volume 153, Issue 7*, pp. 659-665. DOI 10.1093/aje/153.7.659.

36. Huang, S.-K.; Lindell, M. K. & Prater, C. S. Who leaves and who stays? A review and statistical meta-analysis of hurricane evacuation studies. *Environ. Behav.*, **2016**, *Volume 48, Issue 8*, pp. 991-1029. DOI 10.1177/0013916515578485.

37. Huang, S.-K.; Lindell, M. K.; Prater, C. S.; Wu, H.-C. & Siebeneck, L. K. Household evacuation decision making in response to hurricane Ike. *Nat. Hazards Rev.*, **2012**, *Volume 13, Issue 4*, pp. 283-296. DOI 10.1061/(ASCE)NH.1527-6996.0000074.

38. Institut National de la Statistique et des Études Économiques (INSEE). Recensement de la population. **2016**, Available online at https://www.insee.fr/fr/information/4172214.

39. Institut National de la Statistique et des Études Économiques (INSEE). Dossier complet – Commune de Paris 15ème District (75115). **2019**, Available online at https://www.insee.fr/fr/statistiques/2011101?geo=COM-75115.

40. Jumadi, J.; Heppenstall, A. J.; Malleson, N. S.; Carver, S. J.; Quincey, D. J. & Manville, V. R. Modelling individual evacuation decisions during natural disasters: A case study of volcanic crisis in Merapi, Indonesia. *Geosciences*, **2018**, *Volume 8, Issue 6:196*, 30 p. DOI 10.3390/geosciences8060196.

41. Kolen, B. Certainty of uncertainty in evacuation for threat driven response. Principles of adaptive evacuation management for flood risk planning in the Netherlands. **2013**, PhD Thesis, Radboud University, The Netherlands, 315 p. Available online at https://repository.ubn.ru.nl/bitstream/handle/2066/115713/115713.pdf.

42. Kreibich, H.; Müller, M.; Schröter, K. & Thieken, A. H. New insights into flood warning reception and emergency response by affected parties. *Nat. Hazards Earth Syst. Sci.*, **2017**, *Volume 17*, pp. 2075-2092. DOI 10.5194/nhess-17-2075-2017.

43. Lechner H. N., Rouleau M. D. Should we stay or should we go now? Factors affecting evacuation decisions at Pacaya volcano, Guatemala. *Int. J. Disast. Risk Re.*, **2019**, *Volume 40*. DOI 10.1016/j.ijdrr.2019.10.101160.

44. Legifrance. Order of December 30, 2011 on safety regulations for the construction of high-rise buildings and their protection against the risk of fire and panic, art. GH 43, § 1, JORF n° 0015 01/18/2012, France, 2012.

45. Lhomme, S. ; Vuillet, M. ; Cariolet, J.-M. & Del Mondo G. Des outils d'aide à la décision pour faire face aux défis de la mobilité dans le cas de crues à cinétique lente. 15ème Colloque Géorisques : « Résilience et adaptation aux catastrophes naturelles », Montpellier, France, January 22, **2019**.

46. Lim, M. B. B.; Lim, H. R.; Piantanakulchai, M. & Uy, F. A. A household-level flood evacuation decision model in Quezon City, Philippines. *Nat. Hazards*, **2016**, *Volume 80, Issue 3*, pp. 1539-1561. DOI 10.1007/s11069-015-2038-6.
1055 47. Lindell, M. K. & Perry, R. W. The Protective Action Decision Model: Theoretical Modifications and Additional Evidence. *Risk Analysis*, Vol. 32, No. 4, **2012**. DOI: 10.1111/j.1539-6924.2011.01647.x.

1058 48. Lindell, M. K. & Perry, R. W. Behavioral foundations of community emergency planning. **1992**, Hemisphere Publishing Corp, Washington, DC, 309 p.

1060 49. Lindell, M. K.; Arlikatti, S. & Huang, S.-K. Immediate behavioral response to the June 17, 2013 flash floods in Uttarakhand, North India. *Int. J. Disast. Risk Re.*, **2019a**, *Volume 34*, pp. 129-146. DOI 10.1016/j.ijdrr.2018.11.011.

1063 50. Lindell, M. K.; Lu, J.-C. & Prater, C. S. Household decision making and evacuation in response to hurricane Lili. *Nat. Hazards Rev.*, **2005**, *Volume 6, Issue 4*, pp. 171-179. DOI 10.1061/(ASCE)1527-6988(2005)6:4(171).

1066 51. Lindell, M.K., Murray-Tuite, P., Wolshon, B. & Baker, E.J. Large-Scale Evacuation: The Analysis, Modeling, and Management of Emergency Relocation from Hazardous Areas, **2019b**, CRC Press, Routledge, 1st edition, 346 p.

1069 52. Lindell, M. K.; Prater, C. S.; Gregg, C. E.; Apatu, E. J. I; Huang, S.-K. & Wu H. C. Households' immediate responses to the 2009 American Samoa earthquake and tsunami. *Int. J. Disast. Risk Re.*, **2015**, *Volume 12*, pp. 328-340. DOI 10.1016/j.ijdrr.2015.03.003.

1072 53. Luathep, P.; Suwansunthon, A.; Sutthiphan, S. & Taneerananon, P. Flood evacuation behavior analysis in urban areas. *J. East. Asia. Soc. Transp. Stud.*, **2013**, *Volume 10*, pp. 178-195. DOI 10.11175/easts.10.178.

1075 54. Masson-Planchon S., Reghezza-Zitt. **2017** La planification au défi de l'incertitude. Faire face à l'inimaginable. Dans : La gestion de crise à l'épreuve de l'exercice EU SEQUANA. **2017**, sous la direction de November, V. & Créton-Cazanave, L., La Documentation Française, Paris, ISBN :978-2-11-010177-8, 237 p.

1079 55. McCaffrey, S., Wilson, R., & Konar, A. (2018). Should I stay or should I go now? Or should I wait and see? Influences on wildfire evacuation decisions. Risk analysis, 38(7), 1390-1404.

1081 56. McLennan, J., Ryan, B., Bearman, C., & Toh, K. (2019). Should we leave now? Behavioral factors in evacuation under wildfire threat. Fire technology, 55(2), 487-516.

1083 57. Mesa-Arango, R.; Hasan, S.; Ukkusuri, S. V. & Murray-Tuite, P. Household-level model for hurricane evacuation destination type choice using Hurricane Ivan data. *Nat. Hazards Rev.*, **2013**, *Volume 14, Issue 1*, pp. 11-20. DOI 10.1061/(ASCE)NH.1527-6996.0000083.

1086 58. Mileti, D. S. & Beck, E. M. Communication in crisis: Explaining evacuation symbolically. *Commun. Res.*, **1975**, *Volume 2, Issue 1*, pp. 24-49. DOI 10.1177/009365027500200102.

1088 59. Mileti, D. S. Factors related to flood warning response. US – Italy research workshop on the hydrometeorology, impacts and management of extreme floods, Perugia, Italie, November **1995**, 17 p. Available online at https://www.engr.colostate.edu/ce/facultystaff/salas/italy/papers/46mileti.pdf.
1092 60. Murray-Tuite, P. & Wolshon, B. Evacuation transportation modeling: An overview of research, development, and practice. *Transp. Res. Part C : Emerg. Technol.*, **2013**, *Volume 27*, pp. 25-45. DOI 10.1016/j.trc.2012.11.005.

1095 61. Navarro, O.; Chaves, L.; Pineres Sus, J. D. & Noreña Betancur, M. I. Risk perception and coping strategies in population exposed and not exposed to flooding risk. *Interam. J. of Psychol.*, **2016**, *Volume 50, Issue 3*. DOI 10.30849/rip/ijp.v50i3.62.

1098 62. November, V. & Créton-Cazanave, L. La gestion de crise à l'épreuve de l'exercice EU SEQUANA. **2017**, La Documentation Française, Paris, ISBN:978-2-11-010177-8, 237 p.

1100 63. Organisation for Economic Cooperation and Development (OECD). Preventing the flooding of the Seine in the Paris–Ile de France Region. Progress made and future challenges. **2018**, OECD Publishing, Paris, 158 p. Available online at https://www.oecd.org/gov/risk/preventing-the-flooding-of-the-seine-2018.pdf.

1104 64. Organisation for Economic Cooperation and Development (OECD). Seine Basin, Ile-de-France: Resilience to large scale flood s. Main results and recommendations. **2014**, OECD Publishing, Paris, 23 p. Available online at https://www.oecd.org/gov/risk/Flood-risk-management-seine-river-executive-summary.pdf.

1108 65. Parker, D. J. Flood warning systems and their performance. **2017**, Oxford Research Encyclopedia of Natural Hazard Science, DOI 10.1093/acrefore/9780199389407.013.84

1110 66. Parker, D. J.; Priest, S. J. & Tapsell, S. M. Understanding and enhancing the public's behavioral response to flood warning information. *Meteorol. Appl.*, **2009**, *Volume 16, Issue 1*, pp. 103-114. DOI 10.1002/met.119.

1113 67. Paul, B. K. & Dutt, S. Hazard warnings and responses to evacuation orders: The case of Bangladesh's cyclone Sidr. *Geogr. Rev.*, **2010**, *Volume 100, Issue 3*, pp. 336-355. DOI 10.1111/j.1931-0846.2010.00040.x.

1116 68. Peretti-Watel, P. Sociologie du risque. **2000**, Armand Colin, Paris, 286 p.

1117 69. Piatyszek, E. & Karagiannis, G. M. Model-based approach for systematic risk analysis of local flood emergency operation plans: A first step toward a decision support system. *Nat. Hazards*, **2012**, *Volume 61, Issue 3*, pp. 1443-1462. DOI 10.1007/s11069-011-0079-z.

1120 70. Reghezza M. Réflexions autour de la vulnérabilité métropolitaine : la métropole parisienne face au risqué de crue centennale. Thèse de doctorat, Université Paris X-Nanterre, **2006**, Nanterre, 384 p.

1122 71. Riad, J. K.; Norris, F. H. & Ruback, R. B. Predicting evacuation in two major disasters: Risk perception, social influence, and access to resources. *J. Appl. Soc. Psychol.*, **2006**, *Volume 29, Issue 5*, pp. 918-934. https://doi.org/10.1111/j.1559-1816.1999.tb00132.x

1125 72. Rigos A., Mohlin E., Ronchi E. The cry wolf effect in evacuation : A game theoretic approach. *Physica A : Statistical Mechanics and its Applications*, Volume 526, 15 July **2019**, 120890, https://doi.org/10.1016/j.physa.2019.04.126
1128 73. Ronchi, E., Nilsson, D. Modelling total evacuation strategies for high-rise buildings. Build. Simul. 7, 73–87 (2014). https://doi.org/10.1007/s12273-013-0132-9

1130 74. Ronchi, E., Nilsson, D. Fire evacuation in high-rise buildings: a review of human behaviour and modelling research. *Fire Sci Rev* 2, 7 (2013). https://doi.org/10.1186/2193-0414-2-7

1132 75. Smith, S. K. & McCarthy, C. Fleeing the storm(s): An examination of evacuation behavior during Florida’s 2004 hurricane season. *Demography*, **2009**, *Volume 46*, Issue 1, pp. 127-145. DOI 10.1353/dem.0.0048.

1135 76. Solis, D.; Thomas, M. H. & Letson, D. An empirical evaluation of the determinants of household hurricane evacuation choice. *J. Dev. Agric. Econ.*, **2010**, *Volume 2*, Issue 3, pp. 188-196.

1137 77. Solis, D.; Thomas, M. H. & Letson, D. Determinants of household hurricane evacuation choice in Florida. In: Proceedings of the Annual Meeting of the Southern Agricultural Economics Association, Atlanta, USA, January 31-February 3, 2009, 23 p.

1140 78. Soltanzadeh, A., Mazaherian, A., Heidari S. Optimal solutions to vertical access placement design in residential high-rise buildings based on human behavior, *Journal of Building Engineering*, **2021**, Volume 43, Nov. 2021, 102856. https://doi.org/10.1016/j.jobe.2021.102856

1143 79. Strahan K. W., Whittaker J., Handmer J. Self-evacuation archetypes in Australian bushfire. *Int. J. Disast. Risk Re.*, **2018a**, pp.307-316, https://doi.org/10.1016/j.ijdrr.2017.10.016

1145 80. Strahan K. W., Whittaker J., Handmer J. Predicting self-evacuation in Australian bushfire. *Environmental Hazards*, (2018b). https://doi.org/10.1080/17477891.2018.1512468

1147 81. Thompson, R. R.; Garfin, D. R. & Silver, R. C. Evacuation from natural disasters: A systematic review of the literature. *Risk Anal.*, (2017), *Volume 37*, Issue 4, pp. 812-839. DOI 10.1111/risa.12654.

1150 82. Toledo, T., Marom, I., Grimberg, E., & Bekhor, S. Analysis of evacuation behavior in a wildfire event. *International journal of disaster risk reduction*, 31, (2018), 1366-1373.

1152 83. Toubin, M.; Laganier, R.; Diab, Y. & Serre, D. Improving the conditions for urban resilience through collaborative learning of Parisian urban services. *J. Urban Plann. Dev.*, (2015), *Volume 141*, Issue 4, pp. 395-408. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000229

1155 84. Wallace, J. W.; Poole, C. & Horney, J. A. The association between actual and perceived flood risk and evacuation from Hurricane Irene, Beaufort County, North Carolina. *Journal of Flood Risk Management*, **2016**, *Volume 9*, Issue 2, pp. 125-135. DOI 10.1111/jfr3.12115.

1158 85. Whitehead, J. C. Environmental risk and averting behavior: Predictive validity of jointly estimated revealed and stated behavior data. *Environ. Resour. Econ.*, **2005**, *Volume 32*, Issue 3, pp. 301-316. DOI 10.1007/s10640-005-4679-5.

1161 86. Whitehead, J. C.; Edwards, B.; Van Willigen, M.; Maiolo, J. R.; Wilson, K. & Smith, K. Heading for higher ground: factors affecting real and hypothetical hurricane evacuation behavior. *Global Environmental Change Part B: Environmental Hazards*, **2000**, *Volume 2*, Issue 4, pp. 133-142. DOI 10.1016/S1464-2867(01)00013-4.
1165 87. Wright, K. C. & Johnston, D. M. Post-earthquake sheltering needs; how loss of structures and services affects decision making for evacuation. In: Proceedings of the 2010 New Zealand Society for Earthquake Engineering NZSEE Conference, Wellington, New Zealand, March 26-28, 2010, p. 7

1168 p.
1169 88. Zaalberg, R.; Midden, C.; Meijnders, A. & McCalley, T. Prevention, adaptation, and threat denial: Flooding experiences in the Netherlands. Risk Anal., 2009, Volume 29, Issue 12, pp. 1759-1778.
1170 DOI 10.1111/j.1539-6924.2009.01316.x.
1171