Tsunami Evacuation Simulation

Case Studies for Tsunami Mitigation at Indonesia, Thailand and Japan

E. Mas

, S. Koshimura

, F. Imamura

, A. Suppasri

, A. Muhari

and B. Adriano

International Research Institute of Disaster Science, Tohoku University, Sendai, Japan

Graduate School of Engineering, Tohoku University, Sendai, Japan

Keywords:

Tsunami Evacuation, Evacuation Simulation, Agent Based Model, 2004 Indian Ocean, 2011 Tohoku Japan.

Abstract:

The 2004 Indian Ocean tsunami and the 2011 Great East Japan tsunami left several lessons for future events.

Both tsunami events conﬁrmed the importance of early evacuation, tsunami awareness and the need of devel-

oping much more resilient communities with effective evacuation plans. To support reconstruction activities

and efforts on developing resilient communities, tsunami evacuation simulation are applied to tsunami mitiga-

tion and evacuation planning. In this paper we highlight the importance of tsunami evacuation simulation as

a tool in disaster management. Case studies of application of a tsunami evacuation model developed by the

authors are presented here. Applications in Indonesia, Thailand and Japan tsunami prone areas are reported.

In addition, challenges and future research directions for tsunami evacuation modeling are brieﬂy discussed.

1 INTRODUCTION

The 2004 Indian Ocean Tsunami (IOT) was reported

and recorded at several locations in Indonesia, Thai-

land and other countries in the Andaman Sea. It

was probably the ﬁrst time for many people around

the world to watch footages of a devastating tsunami

event. (Synolakis and Bernard, 2006) referred to

the surprising images of tourists in Phuket, Thailand,

watching the onslaught of the tsunami without tak-

ing any protective action. Tsunami risk involves not

only the hazard assessment, but also the social com-

ponent of human behavior against disasters. In addi-

tion, evacuation has proofed to be the best way to save

lives against tsunami (Shuto, 2009). The 2011 Great

East Japan Tsunami (GEJT) was one of the biggest

tsunami events in Japans modern history. The large

inundation and tsunami heights reported destroyed

several towns and villages along the coast. Still, there

was a 96% survival rate of people living in the areas

inundated (Suppasri et al., 2012). The unfortunate af-

termath resulted from the 2004 IOT was attributed to

the lack of warning information, tsunami awareness

and early evacuation response. Conversely, the 2011

GEJT left many lessons for future events and con-

ﬁrmed the importance of early evacuation, tsunami

awareness and development of resilient communities

with effective evacuation plans. In this paper, we

present four case studies of tsunami evacuation sim-

ulation conducted to support tsunami mitigation and

evacuation planning in areas at risk.

2 BACKGROUND

Computation power of large amount of data has made

it possible to move the tsunami evacuation modeling

approach from network based (L

ammel et al., 2010)

to grid based (Mas et al., 2012), potential ﬁelds (Me-

guro and Oda, 2005) or hybrid modeling approaches

to optimize calculation times(Kato et al., 2009). Re-

search methodologies are using much more data with

ﬁner levels of granularity through agent based mod-

eling and high performance computing (Wijerathne

et al., 2013). In addition, the interest to incorpo-

rate human behavior in evacuation models has in-

creased (Fujioka et al., 2002; Suzuki and Imamura,

2005; Mas et al., 2012). After the 2004 IOT and 2011

GEJT, tsunami evacuation modelers are looking into

practical applications of simulators, to solve particu-

lar problems that have been present in these events.

Some issues that concern researchers and stakehold-

ers are evacuation timing, bottleneck and congestions

from vehicle evacuation, shelter allocation, evacuees

behavior, risk communication, etc. In addition, the

reconstruction process of tsunami affected areas de-

mand for new evacuation plans following new urban

layouts. The assessment of effective evacuation plans

249

Mas E., Koshimura S., Imamura F., Suppasri A., Muhari A. and Adriano B..

Tsunami Evacuation Simulation - Case Studies for Tsunami Mitigation at Indonesia, Thailand and Japan.

DOI: 10.5220/0005104802490254

In Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH-2014),

pages 249-254

ISBN: 978-989-758-038-3

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

under new urban spatial conditions can be analyzed

and evaluated ﬁrst using evacuation models.

3 CASE STUDIES

Tsunami evacuation simulation is becoming an im-

portant tool to simulate the response to warnings, the

estimation of casualties, the evaluation of evacuation

plans and alternatives for tsunami mitigation. These

experiments are guiding the development of more ef-

fective educational and mitigation programs at many

countries (Bernard et al., 2006). Here, we show sev-

eral examples of case studies of tsunami evacuation

modeling applied to verify, analyze and evaluate real

and predicted tsunami scenarios of evacuation. The

authors had used in a number of cases a tsunami

evacuation model developed in NetLogo (Wilensky,

2001)—an agent-based platform to simulate complex

systems. First, tsunami numerical simulation is con-

ducted using the Tohoku University Numerical Anal-

ysis Model for Investigation of Near-ﬁeld tsunami No.

2 (TUNAMI-N2). In TUNAMI, nonlinear shallow

water equations are discretized on a staggered-leap

frog ﬁnite difference scheme (Imamura, 1996). Re-

sults of the tsunami simulation are integrated to the

NetLogo environment where agents are built to es-

cape from disaster to nearby shelters or exits within

the computational area (Mas et al., 2012; Mas et al.,

2013a).

3.1 Padang, Indonesia

Padang is located on the center of the west coast of

Sumatra in Indonesia. After the IOT in 2004 sev-

eral earthquakes occurred in the southern part of the

Sunda Trench. These events put on alert the city of

Padang and brought attention to an existing seismic

gap. (Imamura et al., 2012) described the assessment

of tsunami hazard in this area based on a megathrust

earthquake scenario for tsunami simulation and using

high-resolution bathymetry and topography data. The

extent of the resulted inundation area covered approx-

imately 25 km

with approximately 235,000 people

at risk of tsunami inundation depths from 3 m to 8

m. In Padang there is a lack of vertical evacuation

facilities inside the predicted inundation area. In ad-

dition, from the experiences of 2007, 2009 and 2010

tsunami warning events, it has been found that resi-

dents mainly use vehicles or motorcycles for evacu-

ation despite trafﬁc jam experiences. Consequently,

in this case study, it was necessary to identify the

time needed by the evacuees to leave the tsunami in-

undation area and assess the routes of possible con-

gestion during the evacuation. Tsunami evacuation

simulation was used to identify times for evacuation

and bottleneck points. A total of 104,352 agents were

modeled within a 15 km

area south in Padang city.

The agent-based model was used (Mas et al., 2012),

modeling residents to respond at different times to

the tsunami threat and evacuate towards the exits and

shelters located on the east side of the city (Figure 1).

Figure 1: Padang city. The areas of evacuation simulation

and, surrounded by circles, the streets which resulted on

congestion.

The timing for evacuation decision was based

on results from questionnaire surveys conducted in

Padang after 2007 and 2009 events. With the amount

of population modeled, and the evacuation behavior

for departure as input in the model, the casualty esti-

mation, time for evacuation and points of bottleneck

were identiﬁed. Fatalities were estimated on 37.7% of

the population simulated in this scenario. In addition,

we identiﬁed several streets congested on the north

part of the study area due to high demand of some exit

points. Areas near the shopping center and the tradi-

tional market center were highly congested due to the

large density of population gathered. Moreover, to

the south of the simulation domain, agents evacuated

to high ground crossing the river through a bridge; the

large number of evacuees resulted on large cues and

crowd congestion.

3.2 Pakarang Cape, Thailand

The next case study is the evacuation simulation of

Pakarang Cape population in Thailand (Mas et al.,

2013b). The Pakarang Cape is in the Khao Lak beach

resort area of Phang Nga province in Thailand, and it

is located at the coast of the Andaman Sea. This area

was devastated by the IOT on 2004. The Thai Me-

teorological Department (TMD) provided population

data and its spatial distribution. A total of 2,649 resi-

dents were modeled based on a night-time population

scenario. The objective was to explore the inﬂuence

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Figure 2: Snapshots of the worst-case scenario, where the

evacuation is towards the shelters located on the right side

of the area.

of vehicles in the evacuation, combined with differ-

ent reaction times from residents. A set of percent-

age of evacuees in vehicles (passengers and drivers)

was assumed among the population to build several

scenarios for simulation. Therefore, 0%, 25%, 50%,

75% and 100% of agents were reduced from the total

population and grouped in 4 passengers to build the

vehicle agent population data. The start time decision

of evacuation followed several departure curves of

Rayleigh distribution characterized by its mean value.

The value of the mean of the distribution, which ﬁts

to the results of a questionnaire survey applied in the

area to 57 residents in different villages among Phang

Nga and Phuket (south of the study area), was 30 min.

In addition to the distribution curve from the question-

naire, three possible scenarios were added: a worst-

case scenario with late evacuation reaction of 120

min mean value—this is the estimated arrival time of

tsunami for the 2004 IOT event in this area—and two

intermediate scenarios with 60 min and 90 min mean

value. The fatality rate was deﬁned as the ratio of es-

timated casualties over the total population involved

in the simulation. Due to the long distance for evac-

uation, approximately 2 km, the worst-case scenario

of evacuation, shown in Figure 2, is a non vehicle

use and on-foot evacuation, added to the late reaction.

The application of tsunami evacuation simulation in

this case study showed the capability of evacuation

modeling to evaluate the feasibility of evacuation. In

this case, twenty scenarios of different starting time of

evacuation and percentage of use of vehicles in evacu-

ation were compared. Results suggest that, due to the

long distance of shelters, small number of population

and sufﬁcient road capacity, vehicle evacuation was

possible and required to ensure safety. Notice that in

a larger population, trafﬁc congestion might be possi-

ble, as we will describe on the following case study.

3.3 Sendai, Japan

(Mas et al., 2012) modeled the evacuation of Arahama

town for the 2011 GEJT. Arahama was a populated

village in Sendai plain area. Approximately 300 fa-

talities were reported in this area, where the tsunami

arrived at approximately 67 min after the earthquake

with a maximum tsunami height over 8 m. The ex-

tent of the inundation area reached as far as 5 km in-

land. In Arahama, the only ofﬁcial Tsunami Evacu-

ation Building (TEB) was the Arahama Elementary

School. The school is a four-story building with ac-

cessible roof. It provided tsunami shelter to approxi-

mately 520 evacuees. The tsunami evacuation simu-

lation was conducted to 2,271 people. The majority

of them, based on pre-tsunami questionnaire surveys

(Suzuki and Imamura, 2005), preferred to evacuate on

vehicles (72%). With 4 passengers per vehicle, 410

cars and 631 pedestrians were modeled. A stochastic

simulation was performed to obtain average outputs

for parameters of interest. A total of 1,000 simula-

tion repetitions were conducted with random initial

spatial distributions of pedestrians and vehicles. The

evacuation start time decision was based on a ran-

domly selected value within boundary distributions

constructed with the mean of distribution ﬁtting stated

preference questionnaires and recorded arrival time of

the tsunami on 11 March, 2011 (67 min) (Figure 3).

Each simulation provides information of the num-

ber of evacuees in shelters, the number of evacuees

who have passed one of the exits and the number of

evacuees trapped by the tsunami with more than a

50% probability of falling by the ﬂow. This informa-

tion, at such level of granularity, is one of the greatest

advantages of agent-based modeling. It is not only

the emergent behavior that is discovered, but also the

details of common agent behavior and possible local

issues that can be identiﬁed. It is difﬁcult to obtain

the exact values observed with the stochastic simula-

tion developed in this study; however, the average es-

timations of survivors and evacuees at the TEB shows

that the model has good capability for representation

of decisions and casualties in the area. The standard

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Figure 3: The area within the boundary distributions con-

tains the possible values and distributions of evacuation be-

havior used in the simulation. The dotted line is the result-

ing evacuee behavior taken from one scenario of the 1,000

repetitions of random selection.

error of predicted values versus the ofﬁcial informa-

tion is 43% for the case of casualty estimation (model:

406, data: 283). Due to the uncertainty in the initial

population distribution, the actual number of passen-

gers in vehicles and the possible use of non-ofﬁcial

buildings as shelters, the casualty estimation could

not be accurately predicted. However, good results

were met for the number of evacuees sheltered at the

TEB with 4% error (model: 498, data: 520) and 7%

for the rest of the population evacuating inland. In ad-

dition, time-history plots of shelter demands and traf-

ﬁc ﬂow through exits were developed in the model to

support on the allocation of future new shelters and

distribution of relief resources.

Figure 4: The last snapshot of simulation showing in white

the areas of trafﬁc congestion during evacuation. Trafﬁc

congestion was conﬁrmed with survivors for the segment in

front of the Tsunami Evacuation Building (TEB).

Trafﬁc was generally observed on the main roads

leading to the exit points, at the entrance of the

tsunami evacuation building and near the bridge at the

channel (Figure 4). Although these bottlenecks did

not occurred at the same time, they were observed at

some point during at least one of the repetitions. All

of these bottlenecks should be identiﬁed as possible

critical points. The application of tsunami evacuation

simulation in this case showed the capability of the

model to identify bottlenecks and verify the process

of evacuation with several behavioral conditions. The

stochastic simulation and the individual level of rep-

resentation in the model gives the modeler a reason-

able amount of data to analysis and identify issues not

only at the large scale, but also on local agent behav-

iors that might not contribute to a safe evacuation.

3.4 Natori, Japan

In (Takagi et al., 2014), the evacuation behavior re-

ported in Natori, Yuriage was simulated to verify the

evacuation process and the reasons for the large num-

ber of fatalities in the area. Yuriage is a small town

near the Natori River in the plain area of Miyagi Pre-

fecture. Before the earthquake, approximately 5,612

residents were living in the area. Due to the earth-

quake, 752 people were killed by the tsunami and 41

are still missing. It was reported in this area that resi-

dents evacuated to nearby shelter areas, however after

tsunami warnings were increased (Japan Meteorolog-

ical Agency (JMA), 2013), some evacuees decided to

conduct a second step evacuation to a far inland shel-

ter. Tsunami arrived when evacuees where moving to

a second shelter and killed some of them. We found

that the number of fatalities could have been reduced

provided people would have evacuated directly to the

second shelter from the start. The model was applied

in two scenarios of simulation: Case A: a scenario as

close as possible to the real evacuation, based on re-

ported data by local authorities and survivors; Case B:

a scenario where the second evacuation was not per-

formed. The actual reported number of fatalities in

the event and the results from simulation are shown

in Table 1. Figure 5 shows the sequence of evacua-

tion at each shelter in Case A, where the Community

Center was already full after approximately 25 min

from the earthquake ( 15:10 JST).

At 15:14 JST, tsunami warning was upgraded for

Miyagi coast from 6 m to above 10 m (Japan Meteoro-

logical Agency (JMA), 2013). Based on information

provided by survivors, approximately 15:30 JST was

the time for evacuees to start moving from the Com-

munity Center to the Junior High School, this agrees

with the time of the tsunami warning upgrade. Thus,

in the model we set 15:30 JST as the time for the sec-

ond evacuation. Similar to simulation results, it was

reported that in the Community Center 43 people sur-

vived (Murakami et al., 2012). In addition, results

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Table 1: Shelter capacity near Yuriage, the outcome of the 3.11 tsunami and results from Case A and Case B. The last column

shows the reduction on fatalities when no re-evacuation behavior was performed.

Shelter Capacity 3.11 Case A Case B A-B

Community Center 300 43 43 300 +257

Yuriage Junior High School 2000 1000 1050 1067 +17

Yuriage Elementary School 2300 870 699 759 +60

Fatalities ** 762 774 436 -338

Figure 5: The time-history of evacuees in shelters from

the simulation. Notice the arrival and second evacuation

to/from the Community Center (blue line).

Figure 6: Yuriage, Natori inundated by tsunami numerical

simulation results. Black dots are evacuees in progress as

pedestrians; purple dots are evacuees on vehicle, red dots

are fatalities caught by tsunami and yellow dots show the

points of bottleneck during the simulation.

show that a total of 257 evacuees were able to leave

the Community Center before the arrival of tsunami,

but only 82 were able to reach the Junior High School

on time (Figure 6).

Some reasons for the number of fatalities may be:

(i) The short time for re-evacuation: each evacuee

conducted a second evacuation in between 15:30 JST

to approximately 15:50 JST—the time when tsunami

arrived to the Community Center; (ii) The trafﬁc jam:

based on survivor’s accounts, the road in front of the

Community Center was congested with vehicles and

people who tried to re-evacuate on car. Conversely,

as in Case B, where evacuees do not perform a sec-

ond evacuation from the Community Center, the total

amount of fatalities could have been reduced approxi-

mately 44%. Tsunami evacuation modeling is a pow-

erful tool that can be applied to understand the effects

of evacuees decisions on the outcomes of their evacu-

ation process. In addition, future evacuation plans and

activities for reconstruction process and urban plan-

ning can be supported by the results provided from

these kind of models.

4 FINAL COMMENTS

Each time when a tsunami occurs, lessons are gath-

ered and shared, then, tsunami research shows a

progress (Shuto, 2009). Similarly, tsunami evacua-

tion research has substantially improved since 2004

IOT. The future of tsunami evacuation research, as

seen by the authors, is the comprehensive approach

of the geophysics of tsunami and the psychology of

the evacuee behavior built into an integrated model-

ing technique. There are several ongoing efforts (Mas

et al., 2012; Mas et al., 2013a; Wijerathne et al.,

2013), however, still data to verify and validate the

human behavior in emergency and models to ade-

quately represent the complexity of the mind are few.

A clear representation of the human complexity into

a computing agent needs to be built from theories in

psychology of disasters and techniques in artiﬁcial in-

telligence simulation. In addition, it is important to

complement the information available with data from

real events to verify and validate models of behavioral

and urban simulation. In the case of tsunami evacua-

tion models, using data from one of the most recorded

events, 2004 IOT or 2011 GEJT, can be conducted

to beneﬁt the validation and improvements of cur-

rent models. In this paper, we highlighted the impor-

tance of tsunami evacuation simulation through case

TsunamiEvacuationSimulation-CaseStudiesforTsunamiMitigationatIndonesia,ThailandandJapan

253

studies of practical application in Indonesia, Thailand

and Japan. Tsunami evacuation feasibility, casualty

estimation, bottleneck identiﬁcation, shelter alloca-

tion and other applications were modeled to support

tsunami mitigation activities.

ACKNOWLEDGEMENTS

We express our deep appreciation to JST-JICA

SATREPS projects, the Ministry of Education, Cul-

ture, Sports, Science and Technology (MEXT) in

Japan, and the International Research Institute of Dis-

aster Science (IRIDeS) at Tohoku University, Japan

for their support.

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