PERFORMANCE ESTIMATION OF EARTHQUAKEEARLY WARNING SYSTEM FOR DISASTERREDUCTION IN WESTERN JAVA, INDONESIAAndiyansyah Z. Sabarani
MEE12604

Supervisor:

Hiroshi INOUE

Abstract
We estimated the performance of future earthquake early warning (EEW) system in Indonesia. We
modeled casualty ratio over natural warning lead time (S-P time). We estimated the lead time by aproposed EEW system. We applied the casualty ratio model to estimate the casualty with and without
EEW system.
We estimated the lead time by a proposed EEW system and applied the casualty ratio model toestimate the casualty with and without EEW system. We obtained the reduction of the casualty by theEEW system in the area with about 34 % and 3% for a scenario of an M8 subduction zone earthquake
and for that of an M7 inland earthquake, respectively. We concluded that the increased lead time by
the EEW can play an important role to reduce the number of casualty by future damaging earthquakes
in western Java.
Keywords: Earthquake Early Warning, Performance Estimation, Disaster Risk Reduction, WesternJava.
1. INTRODUCTION
Although Indonesia has started a Tsunami Early Warning System in 2006 after the giant earthquake
and tsunami in Sumatra on December 26, 2004, there were many large and devastating earthquakes
occurred in Indonesia which caused thousands of people’s deaths and injuries mainly due to collapsesof non-engineered building and houses in Indonesia. Because of this, Indonesia government has a plan
to establish an Earthquake Early Warning (EEW) system in Indonesia. The purpose of the study is toestimate the performance of a future earthquake early warning system in western Java, and has focuseson the estimation the reduction of casualty by implementing the EEW system in western Java in thefuture.
2. DATAIn this study, we used several dataset. The numbers of casualty and building damages from the pastearthquakes in Yogyakarta, Tasikmalaya and Padang are used to model the casualty ratio due toheavily damaged houses in the past earthquake. We used 20 seismometer locations (Fig. 1) of theproposed EEW system by BMKG to estimate the warning performance, and two past earthquake
epicenter locations (Table 1) for the scenario earthquakes. The demography data of western Javaregion in 2010 (BPS, 2010) is also used to estimate the number of URM (unreinforced masonry)
houses and number of collapsed houses by ground shaking estimation from the earthquake and strong
motion scenario. Estimated number of collapsed houses is used to estimate a casualty ratio and thenumber of casualty due to the earthquake and strong motion scenario and EEW performance for risk
reduction



Meteorological Climatological and Geophysical Agency (BMKG) of Indonesia.
National Research Institute for Earth Science and Disaster Prevention (NIED), Tsukuba, Japan.

1Table 1. Scenario earthquake parameters.
Earthquake Scenario

Lat(S)

Lon(E)

Depth(km)Scenario I (Subduction zoneearthquake )7.40

104.6910Scenario II (Inland earthquake)

6.86

106.94

15

3.

Mw
8.0
7.0

THEORY AND METHODOLOGY

3.1 EEW System Performance Estimation
We introduced a new method to estimate the performance ofFigure 1. Candidate of future future EEW system for seismic risk reduction. We assumed two
EEWS seismic station in western earthquake scenarios with M8 for subduction zone and M7 forJava. (BMKG, 2012). Two red inland earthquake to estimate the strong ground motion
stars are the epicenter of two acceleration (PGA) by using attenuation relationship formula by
Si and Midorikawa (1999) and the arrival time of P and S wave atearthquake scenarios.
the capital city of every region and cities in western Java.
The estimated PGA and a fragility curve for URM houses proposed by Khalfan (2013) are used
to estimate the number of collapsed or heavily damaged of the URM houses. The number of URM
houses in every region and city in western Java is estimated from the published ratio of URM houses
in rural and urban area we adopt 0.85 for rural area (Surahman, 2000) and assumed 0.4 for urban area,
number population in every region. We also assumed occupancy density in every house to be 4.
We used data of casualty and damage loss of building houses from past earthquakes in
Yogyakarta, Tasikmalaya and Padang to make a model of casualty ratio over S-P wave arrival time at
the pointed capital city area from the epicenter of each earthquake event.

3.2. Casualty Ratio Modeling
A casualty ratio is defined as the number ofcasualties in one collapsed or heavily
damaged URM house. We conducted amodeling by using earthquake loss datafrom the Yogyakarta earthquake on May 26,
2006,
Tasikmalaya
earthquakeon
September 2, 2009 and Padang earthquake
on September 30, 2009 which is recordedby the National Disaster Management
Agency of Indonesia (BNPB) to obtain themodel. We estimated the casualty ratio in
every region and city affected by each
earthquake by Eq. (1).
We pointed the capital city of affected region toestimate the hypocentral distance, and weestimated the P-wave and S-wave arrival time toestimate the lead time by natural warning in every

Figure 2. Flowchart of the study to estimate
the number of casualty with or without EEW.
region.

2

𝑐𝑎𝑠𝑢𝑎𝑙𝑡𝑦 𝑟𝑎𝑡𝑖𝑜 (𝐶𝑅) =𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑐𝑎𝑠𝑢𝑎𝑙𝑡𝑦 (𝑁𝐶)𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑐𝑜𝑙𝑙𝑎𝑝𝑠𝑒 𝑈𝑅𝑀 ℎ𝑜𝑢𝑠𝑒𝑠(𝐶𝐻)(1)

We plotted the casualty ratio over the lead time and fitted into an exponential distribution, then we use
it as the model to estimate casualty ratio in every region in western Java. Eq. (3) defined the casualty
ratio as an exponential function of lead time. It means when people can have adequate lead time toevacuate before the strong ground shaking reached the area, the casualty ratio can be reduced
3.3 EEW -Lead Time Estimation
The lead time in EEW system is defined as the time difference between the S-wave arrival time at acertain area and the time of initial warning.
∆𝑡 = 𝑡𝑠𝑟 − 𝑡𝑝𝑠 − 𝑡𝑝𝑟𝑜𝑐 − 𝑡𝑑𝑠(2)

where, ∆𝑡 is a lead time or initial warning issuance, 𝑡𝑠𝑟 is the arrival time of S-wave at the region oftarget area (s), 𝑡𝑝𝑠 is the arrival time of P-wave at the closest distance of seismic station from theepicenter, 𝑡𝑝𝑟𝑜𝑐 is time for the system to estimate the earthquake parameter (hypocentral distance,magnitude and intensity) that is estimated as 3 seconds by using the first 3 seconds of the P-wave
arrival at the closest station from the epicenter (Nakamura, 1988; Kanamori, 2005; Wu and Kanamori,2005a; Wu et al, 2007; Wu and Kanamori, 2005b; Zollo et al., 2006; Wu and Kanamori, 2008a), and𝑡𝑑𝑠 is the time that is used for warning dissemination processing and it’s assumed as 5 seconds(Yamasaki, 2011). The 5 seconds assumption is based on 5.4 seconds of JMA’s earthquake early
warning issuance to the advance user and 8.6 seconds for general public issuance for Tohoku
earthquake in 2011 (Hoshiba, et al., 2011; Yamasaki, 2011).
Eq. (3) defined the casualty ratio as an exponential function of lead time. It means when people canhave adequate lead time to evacuate before the strong ground shaking reached the area, the casualty
ratio can be reduced.
160140120100806040200

Lead timeTime (s)4.RESULT AND DISCUSSION

0100200300Distance (km)4.1. Results
By following the methodology, we obtained thereduction of the casualty ratio by the EEW systemin the area in average about 34% and 3% for a
scenario of an M8 subduction zone earthquake
and for that of an M7 inland earthquake,respectively. The result is obtained as follow:

EEW
4005004.1.1.
Model for Casualty Ratio
We obtained the casualty ratio over distance for thepast earthquakes (Fig. 4(a)). We modeled the casualty
ratio as a function of the lead time of natural warning(Fig. 4(b)) by assuming homogeneous P-wave andS-waves velocities as 6.8 km/s and 3.4 km/s
respectively (Gunawan, 2010). We neglected theearthquake rupture process to simplify the wave
arrival time estimation at every region. We obtained

Casualty ratio per HD
HousesFigure 3. Lead time of the EEW system.

0.06

Casualty Ratio over Hypocentral
Distance

0.05
0.04

Yogyakarta

0.03

Tasikmalaya

0.02

Padang

0.01
0.00
050

100 150 200 250
Distance (km)Figure 4(a).

30.060.05

0.04

y = 0.1093e-0.162x
R² = 0.5439

0.03
0.02

the casualty ratio over lead time by fitting the model into
an exponential distribution function (Fig. 4(b)).
𝐶𝑅 = 0.1093 𝑒 −0.162(𝑡𝑠−𝑝 )(3)where 𝑡𝑆−𝑃 is P-wave and S-wave arrival timedifference or a lead time with or without EEW at every
region.

0.01
0.0001020Time (s)3040

Figure 4(b).

Figure 4. (a) Casualty ratio from pastearthquakes
overhypocentral
distance. (b) Casualty ratio over leadtime as the model to estimate casualty
ratio in every region of western Java.

4.1.2. Damaged URM Houses due to Estimated StrongGround Motion
Si and Midorikawa (1999) attenuation formula is used
into the scenario earthquakes to obtain the strong groundmotion distribution in western Java. We estimated thenumber of collapsed URM building or heavily damaged
houses due to estimated strong ground motion
acceleration in every region by using fragility curve for
URM houses in Bantul, Yogyakarta (Khalfan, 2013).

Table 2(a). Estimated Number of Casualty by an M8 Subduction Zone
Earthquake Scenario Estimated number.
WithoutEEW

WithoutEEW

4.1.3. EstimatedCasualty
Performance

Table 2 (a) and 2(b)show the number ofcasualty in every
Kepulauan
10100 Karawang
113 72.7
region in western
SeribuJava by using Eq.
Jakarta Selatan
993960.6 Bekasi
30
8 73.3(3).
Then
we
Jakarta Timur
82
2767.1 Bandung Barat
8275
simulatedtheJakarta Pusat
33
1166.7 Kota Bogor
8751 41.4
number of casualty
Jakarta Barat
1064062.3 Kota Sukabumi
25
12 52.0in every region by
Jakarta Utara
441468.2 Kota Bandung
4175
adjusting the leadBogor
408
18055.9 Cirebon
10100time. When theSukabumi
268
11756.3 Kota Bekasi
13130Cianjur
1133569 Kota Depok
9640 58.3
lead time of EEWBandung
16475.0 Cimahi
20100system
has
3
Garut
10100 Tasikmalaya
10100
seconds
longer,Tasikmalaya
10100 Banjar
10100
number of casualty
Ciamis
10100 Pandeglang
1,571
1160 26.2
can be reducedKuningan
10100 Lebak
1,741
1264 27.4
about 41% andCirebon
10100 Tangerang572
384 32.9
when the lead timeMajalengka10100 Serang126
97 23.0
is decreased for 3Sumedang
10100 Tangerang138
66 52.2
seconds,
theIndramayu
10100 Cilegon
148
143
3.4
number of casualty
Subang
10100 Serang
2682583.7
can be reduced
Tangerang
Purwakarta
4
175
119
42 64.7
about 18%. And for
SelatananM7inlandWithoutWith
earthquake
scenario,
Total
EEW
EEW (%)
when the lead time
6,145
4,012 34.7
of EEW system isincreased 3 seconds longer, the number of casualty reduction is increased, become almost 14%. And
when the lead time of EEW system has 3 seconds decreased, the number of casualty reduction isRegency/CityWith
EEW

(%)

Regency/City4WithEEW(%)

become less than 2 % (Table 3).
Table 2(b).
Scenario.

Estimated Number of Casualty by an M7 Inland Earthquake

4.2. Discussions

We obtained theresult by assuming
WithRegency/Cit
With
Regency/City(%)(%)
that the scenario
EEW
yEEW
earthquakes occur
Kepulauan
10370
Karawang
5,528 5,528 0
red in the evening
Seribuor in the morning
Jakarta Selatan 5,5165,5160Bekasi
6,765 6,765 0
time when people
Bandung
Jakarta Timur
6,991
6,99105,487 5,487 0
are awake in URM
Barathouses. We also
Jakarta Pusat
1,926
1,9260Kota Bogor
6,896 6,896 0
assumed that peo
Kota
Jakarta Barat
4,423
4,423
0
2,869 2,869 0
Sukabumi
ple have the same
Kota
response between
Jakarta Utara
3,155
3,15505,265 5,265 0
Bandung
the natural war
Bogor
27,683 27,683 0
Cirebon22290.9
nings and an EEW
Sukabumi
28,726 28,726 0
Kota Bekasi
5,808 5,808 0system.
TheseCianjur
22,094 22,094 0
Kota Depok
7,302 7,302 0
assumptions
wasBandung
9,224
9,224
0
Cimahi
1,493 1,493 0
introduced
toGarut
1,878
814
56.6
Tasikmalaya
139
28
79.9
simplify the humanTasikmalaya
641
160
74.9
Banjar12
1
91.7
behavior before andCiamis
256
36
85.8
Pandeglang
1,166 590
49.4
duringtheKuningan
131
16
87.8
Lebak
2,870 2,748 4.3
earthquake,
beCirebon
235
29
87.7
Tangerang
5,178 4,828 6.8
cause
until
now,
Majalengka
395
95
75.9
Serang343
158
53.9
there is no such
Sumedang1,155624
45.9
Tangerang
3,159 3,029 4.1
data to estimate
Indramayu
273
40
85.5
Cilegon128
3572.7
casualty caused bySubang
2,312
1,655
28.4
Serang371
151
59.3
heavily damaged
Tangerang
Purwakarta
2,746
2,746
0
3,381 3,381 0
URM houses due toSelatan
Without
With
strong ground moTotal
EEW
EEW
(%)
tion in Indonesia.
183,952
178,320 3.1
The EEW system
performance
depends[Type a quote from the document or the summary of an interesting point. Youon
theinitialtimeofwarning
Table 3. Estimated casualty reduction by adjusting lead time ofissuance, and physical andEEW system with 3 seconds longer or 3 seconds faster.
psychological condition forhuman response during theEstimated Casualty
Estimated Number of casualty
earthquake.
Reduction with EEWTime of occurrence andEEW system leadsystem and its
without
withdistancefrom epicenter also
time adjustment
adjustments (%)
EEW
EEW
might affect the performance
+3s
-3s
0s
+3s-3sof EEW system. The short
M8 Subduction Earthquake Scenario
distance from epicenter will
6,145
4,012
3,640
5,044 34.740.8
17.9
give strong shaking where
M7 Inland Earthquake Scenario
people cannot stand on their
183,952 178,320 158,492 181,321 3.1
13.8
1.4
foot, and they have inadequate
time to have response onevacuation. An earthquake canoccur in daylight, evening, midnight, early in the morning and in any physical condition we have at thetime like we are working, studying, sleeping, playing, we are in the house, in a building or in a yard
and etc. which can affect evacuation performance during the earthquake. These possibility conditions
WithoutEEW

WithoutEEW

5

have taken account on the evacuation performance.
5. CONCLUSIONS
We concluded that the lead time has an important role to reduce the number of casualty for any
location of the earthquake (Fig. 3, Fig. 4(b), Table 3 and Table 4). This is because we considered
human response on saving their live with the lead time they have at the time when the earthquake
occurred in the morning or evening when the people are awake in URM houses.
The EEW system can give a longer lead time if some component of the earthquake parametersdetermination processing and dissemination time can be reduced. Closest distance of the seismic
station to the potential seismic source also has an important role to gain a longer lead time of thesystem, but for decreasing earthquake parameters determination processing time will be very difficult,because it might effect to the accuracy of the earthquake parameters. Another way to gain a longer
lead time of the EEW system is by installing a very robust and rapid communication system todisseminate the warning.
We need further investigation and research about the method with various scenario earthquakes
to gain more accurate result on the estimation of EEW system performance, so the performance canbe improve to reduce seismic risk in the future. Finally we concluded that future EEW system has apotential to reduce the seismic risk in western Java, Indonesia.

REFERENCES
BMKG, 2012, Earthquake Catalogue in Region from 1821 to 2011 (in Bahasa).
Gunawan, H., et al.,. 2010, ITB.
Hoshiba, M., et al., 2008, EOS, 89, 73-80.
Hoshiba, M., et al., 2011, Earth Planet and Space, 63, 547-551.
Kamigaichi, O., 2004, Journal of Japan Association for Earthquake Engineering, 4, 134-137.
Khalfan, M., 2013., Mc Master University, paper 7691.
Nakamura, Y., 1988, In Proceedings of the 9th WCEE VII.
Si, H., and Midorikawa, S., 2000, 12th WCEE.
Surahman, A., 2000, 12th WCEE.
Website: Dibi, BNPB, http://dibi.bnpb.go.id
Website: National Statistical Beareau of Indonesia (BPS) http://bps.go.id.
Website: BPBD, http://bpbdprovsumbar.blogspot.com/.
Wu, Y.-M., and Kanamori, H., 2005a. Bull. Seis. Soc. Am, 95, 347–353.
Wu, Y.-M., and Kanamori, H., 2005b. Bull. Seis. Soc. Am, 95, 1,181–1,185.
Wu, Y.-M., and Zao, L., 2006, Geophysical Reasearch Letter, 33, L116312,
doi:10.1029/2006GL026871.
Wu, Y.-M., et al., 2007. Geophysical Journal International, 170, 711–717.
Wu, Y.-M., and Kanamori, H., 2008. Sensors 8, 1–9.
Yamasaki, E., 2011, What we can learn from Japan’s earthquake early warning system.

6