Journal of Natural Disaster Science, Volume 26, Number 2, 2004, pp73-80

Micro Model Simulation Tools for Performance-based Design ofa Flood Risk Management System
Tomoharu HORI and Michiharu SHIIBA
Department of Global Environmental Studies, Kyoto University(Received for 10 Jun., 2004 and in revised from 7 Jan., 2005)

ABSTRACT
Two types of micro simulation tools for the performance-based design of a flood risk management system havebeen developed; a micro model-based simulation system of flood evacuation, and an object-oriented simulation
model of communication processes among disaster prevention agencies. The micro simulation model of floodevacuation is based on parameterization of people’s attitudes to flood risk and recognition of danger during flooding. The relationship of the information issued, changing conditions during floods and people’s action isexpressed in fuzzy inference rules. Parameterization of these mental factors enabled us to simulate the independent evacuation of people based on no information from authorities and the neglect of official information. Usinga communication process simulator, disaster prevention agencies were modeled as objects which have action rulesets as described in local disaster preparedness plans. Through simulation of communication processes during aflood disaster, concentration of the path of information and unbalanced element task descriptions can be
expressed quantitatively.

1. INTRODUCTION
Water-related hazards occur where natural systems meetsocial ones. To assess the disaster preparedness of societies correctly, it is essential to know the relationship of and interactions
between the two systems. From the viewpoint of hazard mitigation, the relationship between natural and social systems can be
simplified as in Fig. 1. Natural disasters are defined as situations
in which phenomena which occur in natural systems result in unfavorable effects on social systems. Social systems can be grouped
in two categories; residents who suffer mainly from a natural phenomenon; and public agencies responsible for damage mitigation
work. Residents also attempt to mitigate damage caused by a natural phenomenon, and occasionally governmental agencies may beseriously damaged. Therefore, to manage flood risk events of low
frequency but high impact, it is not sufficient to adopt only structured mitigation measures such as dams and dikes. Emergency
responses such as on-site mitigation work and evacuation are crucial in preventing loss from becoming catastrophic. Estimations of
the disaster mitigation potential of social systems require the quantitative measurement of the extent to which damage caused by natural hazards can be mitigated by emergency preparedness work
undertaken by social systems. Therefore, it is necessary to knowhow elements of disaster prevention systems can or cannot act
against disasters.
To organize non-facility-based countermeasures well, however, is difficult. The reasons why are as follows:
1. Countermeasures comprise evacuation, and emergency
responses by residents and disaster prevention agencies. All
include some type of action by people. Designing a system,
the performance of which is strongly affected by people’s

reactions, is always difficult as those actions depend on various factors and their mechanisms are difficult to express quantitatively.
2. A catastrophic flood hazard is caused by low frequency,
severe rainfall events, therefore people seldom experience theactual phenomena. Because those events occur with very low
probability, our knowledge of them inherently contains much
uncertainty.
It must be noted that these two points are not independent.
The rarity of experience renders people’s reactions more uncertain
and they take less notice of such events.
To cope with inherent difficulties and to design an integrated
flood risk management system, it is important to fashion a realistic
simulation tool both of natural disaster events and of people’s reactions to them. Simulations of various cases can be of great help indesigning a risk management system for flooding of low frequency
and in stimulating people’s imaginations of possible high risk situations.
Based on these considerations, we developed two simulation

Fig. 1 Disaster prevention and mitigation systems.

KEY WORDS: flood disaster, simulaton, micro model, evacuation, local disaster preparedness

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models of people’s reactions to flood disasters. One is a micro
simulation model of flood evacuation, the other an object-oriented
simulation model of communication processes during a flood hazard.
The micro simulation model of flood evacuation is based onparameterization of people’s attitudes towards flood risks and therecognition of danger during flooding. The relationships among
information issued, changing conditions during flooding, and people’s actions are expressed by fuzzy inference rules. The parameterization and fuzzy rule expressions of these mental factors enable
simulation of those cases in which people follow or neglect the
information provided by authorities. Evacuation simulations undervarious flood condition scales clarify the safety and risk at eachpoint in the floodplain, providing key information for the design ofemergency response systems. That information can be used toopen minds to the possible dangers and to risk communications.
Actions of disaster prevention agencies also are very important for flood disaster preparedness and in the reduction of casualties. In Japan, the post-disaster emergency actions to be taken bythese agencies are defined in the local disaster preparedness plansof the local governments; therefore a computer model which simulates the behavior of those agencies based on action patterns regulated by the disaster preparedness plan, would be of great help inanalyzing regional flood hazard preparedness and in revising theaction plans. Many experiments on flood response work undervarious conditions can be performed on a computer, which are
impossible to do during field training.
Activities undertaken by disaster prevention agencies consistof such types as information transmission, establishment of evacuation centers, securing supplies, and the direction of refugees. Of
these, the tasks of the collection and transmission of information
are said to be the most important in particular in the initial stagesof disasters. Actually, a large number of descriptions in disaster
preparedness plans are devoted to tasks related to information collection and communication. The lack of communication and inadequate transmission of information often lead to delays in emergency response work. To analyze information processing tasks inemergency response quantitatively, we designed an object-oriented
simulation model of the communication processes that operate
among disaster prevention agencies during flood disasters.
2. MICRO SIMULATION MODEL OF
FLOOD EVACUATION
2.1 Simulation Approaches to Flood Evacuations
In terms of casualty reduction, evacuation is an important
countermeasure against flood inundation. To help people evacuatesmoothly during inundation, a great deal of effort goes into the distribution of hazard maps and the development of flood prediction
and warning systems. It is desirable that exact warning be issued
in sufficient lead time and that everyone is evacuated before inundation occurs. In past flooding, however, many residents did not
follow the official instructions, and some were unable to obtainwarnings (e.g., Michiue, 1979; Imamoto et al., 1983; Katada et al.,2001). It therefore is very important to understand the characteristics of people’s evacuation actions and to simulate evacuation
under various conditions.
Field surveys are an important approach to determining evac-

uation problems in flood dusasters and the relationship between
people’s mental attitudes and their actions for evacuation are wellreported by survey (Michiue, 1979; Imamoto et al., 1983; Katada
et al., 2001). Simulation studies also have been carried out incorporating the interaction between inundation level and refuge
behavior (Nishihara, 1983; Takahashi, 1989).
Those models treat the target residents as a homogeneousgroup in which all respond to the same information in the samemanner. Field investigations, however, show that residents’ mental attitudes toward flood disasters are important in determining
people’s responses to flood conditions and disaster information.
We therefore have built a model which can simulate people’s mental decision processes on evacuation through parameterization ofmental factors related to decision making during evacuation.
2.2 Determining Factors in Flood Evacuation Actions andTheir Modeling
Many factors may be involved in people’s evacuation actions:the attitude towards disasters, life style, previous experience offlood damage, and knowledge about where to evacuate to. For
simplification, the process of evacuation is as follows:
Each family living in an area near a river has its own attitude
toward flood disasters and individual life style, which become theinitial conditions for evacuation. Behavioral patterns of these families are affected by the physical conditions of the flooding such as
inundation level, and are controlled by information obtained fromthe authorities. The behavior of other families also may affect thedecisions and actions of an individual family. To provide evacuation information requires a method of real time control of evacuation. After evacuation begins, the inundation level affects the
speed of movement, and the behavior of other families also hassome influence on a family actions.
The conceptual relationships among the factors which affectpeople’s decisions whether to evacuate are shown hereafter by initial evacuation action condition introduced by numerical parameters and by fuzzy decision making models. The proposed complete model framework for evacuation simulation is as follows:(1) Initial Conditions for Evacuation Action
One of the most important factors in a family’s normal attitude to a flood disaster is its general recognition of flood risk. This
attitude may rest on prior knowledge and experience of floodings
and becomes the initial condition in its evacuation actions during aflood. To express a family’s attitude to a flood disaster quantitatively, a numerical parameter, “outlook on flood disasters” vi,which takes a value from 0 to 1, is introduced. Its value expresses
to what extent a family i estimates the danger of flooding; the closer vi is to 1, the more dangerous flooding is felt to be..
Another important attribute that affects the decision process toevacuate is the way people react to information provided by theauthorities. Reactions to this type of information is determined byfactors such as the content of the information, when issued, andpeople’s previous experiences. For model simplicity, however, it
is assumed that reactions are based only on their reliance on theinformation received; a family which has much confidence in theauthorities tends to decide to evacuate sooner based on the information issued, whereas families which do not have much reliancein them tend to wait and see how things go rather than directly follow the information. These differences in reactions are brought

MICRO MODEL SIMULATION TOOLS FOR PERFORMANCE-BASED DESIGN OF A FLOOD RISK MANAGEMENT SYSTEM

about by individuals’ characters and experiences. The numericalparameter information reliance, si, is used to express the attitude ofa family, i, to the information. Information reliance has a value
close to 1 when the family has much confidence in the information
received and is 0 when it has none.
(2) Decision process to evacuate
Until a family decides to evacuate, the process can be regarded as a mental one in which external factors act on the initial conditions. A family changes its feeling of danger with time based on
rainfall, inundation situations and the information obtained. If thesense of danger exceeds a certain level, the family will decide toevacuate at that time. In contrast, a family that does not feel danger may not evacuate even if it has received an evacuation order.
The decision process of whether to evacuate, therefore, isexpressed by the combination of the family’s sense of danger andinformation recieved.
To incorporate this relationship in the evacuation model, thenumerical parameter “danger recognition rate”, d i(t), is introduced.
Its value changes with time and the flood situation, denoting towhat extent family i feels danger of flooding at time t. The larger
the value, the more dangerous family i considers the current situation. The value 0 denotes that the family feels no danger in the situation. It must be noted that different families have different values of danger recognition based on their usual attitudes to floodrisk. As the usual mental attitude of each family towards flood disasters is expressed by the numerical parameter “outlook on flood
disaster”, v i, it is reasonable to compute danger recognition rate attime t from the value of v i giving the following equation by which
to calculate the danger recognition rate at time t;d i (t ) = vi ◊ pi (t )(1)where p i(t) is the subjective probability that flooding corresponding to v i will occur. The value of p i(t) changes each moment
according to the inundation conditions and information provided,
whereas v i remains constant during the event. A family with asmall “outlook on flood disasters” value cannot have a large “danger recognition rate” even if the subjective probability, p i(t),becomes 1. In equation (1), the danger recognition rate cannot
take a value larger than a family’s “outlook on flood disaster”,
which means that people are limited by their experience in envisioning a possible disastrous situation.
Subjective probability p i(t) changes with flood conditions ofrainfall and the inundation level. A fuzzy inference rule expresses
the relationship between subjective probability and the flood. This
inference rule is expressed as
If r(t) is Aj and Δh i(t) is Bk then Δpi(t) is Cl(t)where r(t) is the rainfall intensity at time t, Δh i(t) the inundation
depth at the house site of the i-th family at time t, and Δpi(t) is thechange in subjective probability for family i. Symbols Aj, Bk and Cl
respectively are fuzzy scopes of r(t), Δh i(t) and Δpi(t).
(3) Complete framework of the flood evacuation simulation model
Based on the expressions of the initial and mental factors proposed earlier, the micro simulation system of flood evacuation
shown in Fig. 2 was developed. It is composed of six knowledgebased systems, the outlines of which are given in Table 1. The
evacuation simulation flow is75

1. Each family has its own attributes; outlook on flood disasters,
information reliance, number of family members, knowledgeof safe places and the route to them.
2. When the flood event starts, the change of p i(t) is computedby means of the fuzzy inference rules. The variables input tothe inference rule set are rainfall, inundation level at time t,
and the increase in inundation level from time t−Δt. The
increment of p i(t) then is modified based on available information and reliance on that information.
3. The danger recognition rate is computed by equation (1), afterwhich
4. The action at time t is determined from the combined danger
recognition rate and information received.
The model is coded as an object-oriented one. By using“class” to express the common architecture of families, various
family models having the same structure but different attributesand data are easily generated by a computer.
2.3 Typical Model Outputs and Discussion
The developed micro evacuation simulation model wasapplied to the model area shown in Fig. 3. Flood water begins to
overflow the embankment at 7 o’clock in the evening. Fifty families live in the area, as shown in the figure. Because no calibration
is made in this test, such attributes of each family as outlook on
flood disasters are given randomly.
Fig. 4 shows temporal changes in the numbers of families on
their ways to a safe place and of those who have completed evacuation. Fig. 4 (a) shows the case for an evacuation order issued at
7:30. Because the area inundated is limited, and the inundationlevel is not very high at this time, people do not immediately follow the order. In the case of an order issued at 8:30 (Fig. 4 (b)),
there are two peaks showing the number of evacuating families.
The first peak is produced by families that independently decided
to evacuate based only on the inundation situation, the second by
the families which made the decision after recieving the evacuation
order.
Those findings confirm that the micro evacuation model cansimulate cases in which some people independently decide to evacuate and others in which an evacuation order is neglected, as often
reported in field investigation studies(Michiue, 1979; Imamoto etal., 1983; Katada et al., 2001).
3. AN OBJECT-ORIENTED SIMULATION MODEL
OF THE COMMUNICATION PROCESS OPERATING AMONG FLOOD DISASTER PREVENTION AGENCIES
3.1 Plans for Local Disaster Preparedness and Evaluations
In Japan, emergency preparedness and response works arecarried out by public agencies based on the plans for disaster preparedness established by local governments. These plans arerevised every year and usually are very large, consisting of several
hundreds of pages or more (Kyoto Prefecture Disaster Prevention
Council, 1995). This means that disaster management systems
have become large and complicated. This raises some basic questions:
1. Are the large numbers of actions ruled in the plan definedconsistently?

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Fig. 2 Complete micro model structure for flood evacuation simulation.

Table 1. Outline of knowledge-based systems used in the micro simulation model of flood evacuation.

MICRO MODEL SIMULATION TOOLS FOR PERFORMANCE-BASED DESIGN OF A FLOOD RISK MANAGEMENT SYSTEM

2. Can government agencies actually perform the functions indicated in the plans?
3. How can the effect of the on-site emergency response works
by these agencies be measured?
Most local governments carry out yearly field training for
emergency response work . Field training can be considered a tool
for the evaluation of damage mitigation systems, but it is executedsolely in enclosed space and under fixed conditions. It cannot simulate actual post-disaster work done under various external conditions. Consequently, studies of local disaster preparedness cannot
but be based on a descriptive approach. Experiences during theHanshin-Awaji great earthquake showed that unexpected external
conditions can have serious effects on disaster response work. For
example, traffic jams caused by post-disaster actions caused problems in implementing mitigation work by public agencies (Kansai
Chapter of JSCE, 1997, 1998).
Computer simulation provides a powerful tool for the analysisof large and complicated systems, including external conditions.
In this study, we developed an object-oriented model which simulates the communication process operating among the public agencies responsible for disaster mitigation work.
3.2 Rule Expression of Provisions in Local Disaster
Preparedness Plan
A large number of descriptions given in disaster preparedness: safety area
: model familyplans are devoted to the way information is transmitted, evidenceof the importance for disaster response work of information processing and communication during and immediately after a natural
hazard. We designed both an inference engine which translates theprovisions of disaster preparedness plans into rule format, allowing
selection of appropriate actions based on the rule base and an
object model of public agencies in charge of damage mitigation
work.
Typical action patterns of public agencies in terms of information transmission as described in a plan can be expressed as a rule
comprised of information receiving, processing of received information, and sending new information to other agencies (Fig. 5). A
rule for this kind of action is expressed by three attributes:
1. type of information received,
2. actions to be taken within the agency,
3. new information to be transmitted to other agencies.
We composed a list expression with keywords as a rule format
to express these attributes. For example, the provision that “Incase of a flood warning transmitted from the section of river affairs
in the local government, construction offices should report the situation to that section” is translated into the rule:((name (Uji-construction-office))(condition ((flood-warning@southern-part-of-Kyoto
section-of-river-affairs))(inner (grasp-the-situation))(notification ((situation-in-the-jurisdiction
section-of-river-affairs))(reference (Kyoto-3-7)))The list which follows the keyword “name” is the name of theagency. Words which follow the keywords “condition”, “inner”,“notification” respectively signify the information which is transmitted to that agency, the actions to be taken within it, and new
information to be sent to other agencies. The “reference part” indicates the volume and section number under which the rule isdescribed in the local disaster preparedness plan.

Input

OutputPublic Agencies

Information
Fig. 3 Model zone for application of the micro simulation model offlood evacuation.

on the way
complete

19:30

20:00

20:30

21:00

(a) Evacuation order issued at 7:30

21:30

Fig. 5 Typical action patterns of public agencies.

60
50
40
30
20
10
0
time 19:00
Number of households

Number of households

60
50
40
30
20
10
0
time 19:00

77

on the way
complete

19:30

20:00

20:30

21:00

(b) Evacuation order issued at 8:30

Fig. 4 Typical evacuation simulation results.

21:30

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T. HORI and M. SHIIBA

We developed an inference engine which selects the appropriate action according to the information input through a search
process in a rule base which stores action rules written in the above
format. Public agencies are defined as objects which have action
rules based on a disaster preparedness plan, an inference engine,
and an interface of communication with other agencies. Class
structures of an agency model are shown in Fig. 6. Class“Agency” is a model of governmental agencies responsible for disaster response work and has instances of the classes“RuleManager” and “Communicator”. Class “RuleManager” is aninference engine which selects an appropriate rule from the rulebase which stores provisions of a local disaster preparedness plan,
as instances of the class “Rule”. Class “Communicator” represents
the communication interface with other instances of the class“Agency”. “Agency Manager” is a class designed for managing
the actions and communication processes in and among agencies.
3.3 Complete Structure of the Post-Disaster Work SimulationModel
To simulate total post-disaster emergency work, models of theresidents’ layer and natural systems must be designed. As for thenatural system, a simulation model of disaster events such as floodinundation is needed. In this study, however, natural events aretreated as a disaster scenario rather than as an actual simulation
because the study focuses on the information network formed at
the time of a disaster. Because many provisions in local disaster
preparedness plans are described in abstract manner, detailed data
as inundation water levels at each time and location are not needed. A natural system model has disaster events that are describedby the type of event, its location and its beginning and endingtimes (Fig. 7). Obviously detailed simulations of disaster events innatural systems are necessary when actual disaster mitigation
activities such as flood response work are considered. These are
topics for future studies.
Interaction between the social and natural systems in the present simulation scheme is a recognition process of a disaster situation that drives information processing and information transmission among public agencies. There are two kinds of driving information; disaster information issued by agencies external to theagency models and information which agencies within the simulation model obtain by themselves. An example of the former is thewarning information issued by meteorological agencies. External
agencies process their own data and issue warnings of disasterevents, and it is not practical to incorporate such processes in the
post-disaster work simulation scheme. Those agencies therefore
also are modeled as objects that have scenarios about the information they should issue. In contrast, the disaster recognition

processes of public agencies chosen for simulation should beincorporated in the model. This is done by adding a detecting
function for disaster events to the previously designed agency
model. The agency model detects disaster events managed by thenatural system model as a scenario according to a specified probability. The residents’ layer also is modeled that way. It detects a
disaster event according to the specified probability then reports itto the public agency model.
Decisions made by public agencies also are important, in particular when the provisions of disaster preparedness plans allow
some choices to decision makers under certain conditions. For
example, under the provision that “According to requests by city
offices, the governor can request that the Self-Defense Forceshould be called in”, whether to make the request depends on thegovernor’s decision. In such cases it is important to simulate both
the results based on cases in which the decision is made and not
made. In our simulation scheme, these kinds of decisions can be
specified as parameters by the user. The complete structure of thecommunication simulation systems for disaster response work is
given in Fig. 7.
The time required for information processing and transmission
is an important element of post-disaster work simulation. It is very
difficult to specify the actual time, as it depends greatly on thehuman resources and media available, as well as the disaster situation. We used the step concept instead of actual time, as our interest was the density of information transmission, which varies withthe type of agency, and whether a large number of descriptions in
disaster preparedness plans are built consistently. The stepreferred to in this study is a unit cycle required for the processing
and transmission of disaster information given as one provision of
the plan. Note that the time necessary to process even the same
information, referred to here as a step, may vary depending on thedisaster conditions and mobilization of the agency staff. The unit
time necessary for each action is now being investigated andresults will be incorporated in the model in the near future.
3.4 Application
The simulation model designed was applied to the emergency
safety system of the Kyoto prefectural government. The thirty one
public agencies in charge of disaster response work are included in
the model, and related provisions of the disaster preparedness plan
are translated into two hundred rules. The disaster and decision

AgencyManager

control

controldeliveryAgencyAgencyRuleManager

name

RuleManager

name

Rule

Communicator

Rule

Communicator

Rule

Information

Rule

Fig. 6 Structure of public agencies.

Fig. 7 Complete Structure of the post-disaster work simulation model.

MICRO MODEL SIMULATION TOOLS FOR PERFORMANCE-BASED DESIGN OF A FLOOD RISK MANAGEMENT SYSTEM

scenarios are shown in Tables 2 and 3. In these scenarios, a disaster is brought about by severe rainfall, and the resulting break inthe dike along the Kizu River, located at Uji City. The Kizu Riveris one of the main rivers flowing through that area of Kyoto prefecture. Uji, a city neighboring of the river, is one of the most populated cities of the prefecture. In Table 3, three decision scenarios
are devised and the information networks generated by each werecompared.
Typical information transmission simulation results are shownin Fig. 8. The decision scenario is based on case 1 in Table 3.
Lines which connect the agencies specified in the left column
denote information transmission between them at each step.
Closed triangles denote attitude change in an agency and closed
square denotes the decision made by the agency. In this figure thewarning of heavy rainfall and flooding is issued in step one andtransmitted to the fire and disaster section of the Kyoto prefectural
government. This warning information is re-transmitted from that
section to sixteen other agencies. Based on that warning, at step 5
Table 2.

Disaster scenario.

79

the governor decides to mobilize the staff to prepare for post-disaster work, and the decision is transmitted to various sections in thesteps that follow.
Through this kind of simulation based on the scenarios inTable 3, we find that a number of agencies do not send out any
information, they only receive information from other agencies.
These terminals of the information network are of two types; thecase in which the information received requires inner actions, suchas an attitude change, instead of the production of new information, and the other in which are no descriptions to be made basedon the information received. The second type may lead to confusion of agency staff. To quantify the possible confusing situations,a sink ratio, , defined as the ratio of the number of cases with nooutput divided by the total pieces of information received, is introduced. The sink ratios for each public agency are given in Table 4.
It shows that the river section of the prefectural government, thecenter of the disaster response work, produces the maximum sinkratio values. This means that there are fewer descriptions of conTable 3. Decision scenario.

Fig. 8 Typical the simulation results for information transmission.

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Table 4.

Numbers of inputs and outputs, and the sink ratios of disaster mitigation agencies.

crete actions for that section in the local disaster preparedness plan.
Whether detailed descriptions of disaster preparedness andresponse actions are needed for decision making agencies requires
more discussion and deeper analysis. The simulation study proposed here, however, provides a powerful tool with which to analyze whether a plan is well adjusted, and the sink ratio defined here
provides a qualitative measure of the characteristics of a municipality’s disaster response system.
4. CONCLUSION
We designed two types of microscopic simulation models toassess the performance of soft countermeasures against flood disasters. One is a simulation model of evacuation actions in terms offamily units, the other a simulation model of communication
processes among disaster prevention agencies during flood events.
In the evacuation model, various mental aspects which influence a family’s decision making process are expressed by numerical parameters. The interactions among those parameters, flood
conditions, and information provided by authorities are expressedby fuzzy inference rules. Parameterization of some mental factors
enables simulation of both the cases when people neglect information, such as an evacuation order and when they decide without the
information to evacuate based on their own judgment. Throughevacuation simulation, for example, the performance of soft measures, such as warning systems, and some hardware-based countermeasures taken in a flood plain could be estimated in terms of theevacuation success rate. This raises the possibility of making performance-based designs of total flood prevention and mitigation
systems.
A remaining, important problem in the use of the evacuation
simulation model is calibration of the parameters. The model
includes numerical parameters, such as “outlook on flood disasters” and “information reliance”, which express the mental attributes of a family. In the evacuation simulation model applications
presented, we assumed arbitrary values for those parameters. The
model, however, can be calibrated from the results of questionnaire
surveys of residents who have just experienced flood disasters.
For example, it is possible to adjust the parameter values so thaterrors in the evacuation rate in the target area are minimized.
Moreover, the advantage of the micro evacuation model is that we
can determine why each family generated on the computer did or
did not decide to evacuate by checking its danger recognition rate
value. This means that we can calibrate the model not only on thebasis of resulting actions but on people’s ways of making decisions, which makes us possible more realistic simulations.

By simulating public agencies’ communication processes, we
succeeded in estimating quantitatively the sources and sinks of
information transmitted among those agencies during flood eventsand in identifying paths of high information concentration. For a
more realistic emergency response simulation, it is necessary to
add concrete task descriptions and rules for action that are notclearly defined in the local disaster preparedness plan. To address
this problem, we are now engaged in analyzing the concrete tasks
that appear in the field training scenarios for flood disaster
response work conducted by municipalities.
These two models are implemented independently in separate
computer programs at present, but plans are to combine the programs in order to simulate the total human response to a flood hazard.
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Imamoto, H., T. Ishigaki and K. Ohtoshi, 1983. Refuge of people in
Nagasaki flood disaster in July 1982, Annuals. D.P.R.I., Kyoto Univ.,
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Imamoto, H., T. Ishigaki and K. Ohtoshi, 1983. Refuge of people in
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