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Framework For Modeling Mass Disaster, Hamed Assaf

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A Framework for Modeling Mass Disasters
Impact Factor: 1.14 · DOI: 10.1061/(ASCE)NH.1527-6996.0000033
Hamed Assaf
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Retrieved on: 09 December 2015
This is a draft copy of the following paper:
Assaf, H. (2011), “A Framework for Modeling Mass Disasters”. ASCE Journal of Natural Hazards
Review. 12(2): 47- 61. doi:10.1061/(ASCE)NH.1527-6996.0000033.
A Framework for Modeling Mass Disasters
Hamed Assaf, Ph.D., P.Eng, MASCE1
Abstract: The paper introduces a generic mass Disaster Modeling Framework (DMF) to
support integrated socio-physical risk assessment and management of disasters. The
DMF draws on the principles that disasters have many common features regardless of
their instigating hazards and are the outcome of interaction between a hazard and society.
The modular architecture of the DMF weakly couples three key applications: the
Community Simulation Model (CSM), the Hazard Simulation Model (HSM) and the
Evacuation Simulation Model (ESM). The CSM utilizes census, cadastral, and other data
to construct an individual-based representation of the potentially impacted community.
The CSM estimates people and built environment at risk at any given time of the day,
week, and year. The CSM assessment along with the hazard simulation of the HSM feed
into the ESM to simulate the progression of fatalities throughout the event. Dam failures,
for which vivid and detailed accounts of people’s interaction with the incoming flood
exist, served as case studies.
CE Database subject headings: risk assessment; loss-of-life models; evacuation models;
emergency planning; agent.
Assistant Professor, Department of Civil and Environmental Engineering
Faculty of Engineering and Architecture, American University of Beirut
AUB POBox 11-0236 Riad El Solh, Beirut 1107 2020 Lebanon
Tel. (961) 1-350000 x3460, Fax: (961) 1-744462, E-mail: [email protected]
One of the pressing and immortal concerns of people is the sudden advent of a mass
disaster that could bring death and wreck havoc and destruction on their communities.
This concern is shared by societies of different times and places. Whether hazards are
floods, earthquakes, tsunamis, volcanic eruptions, wildfires, hurricanes, terrorist attacks,
or landslides, societies have placed high priorities on efforts to mitigate, prepare for,
respond to and recover from disasters.
Regardless of its nature, a mass disaster episode is the result of an interaction between
two highly complex, dynamic and generally hard-to-predict phenomena: a human
community and a hazard. A massive earthquake in a desolated area or in prehistoric times
is of little significance in comparison to an event involving the death of several thousands
of people as a result of a much less powerful earthquake in a densely populated area.
Consequently, managing the risk of mass disasters requires not only a good
understanding of each phenomenon individually, but also a thorough investigation of the
mechanics of their interaction.
The presented Disaster Modeling Framework (DMF) provides a virtual platform for
creating potential manifestations of a given disaster under different community and
hazard conditions to assess its risk and devise effective mitigation policies. The DMF is
unique in bringing together the two disparate physical and social sciences disaster
research fields into a common ground to study and analyze the socio-hazard aspects of
The DMF incorporates three main components: the Community Simulation Model
(CSM), the Hazard Simulation Model (HSM) and the Evacuation Simulation Model
(ESM). Utilizing census, cadastral and field collected data, the CSM captures details of
the potentially impacted community in terms of the characteristics of individuals, their
families, and associated objects of building, vehicles and infrastructure. The CSM is used
to produce snapshots representing the state of the community at selected times of the year
which serve along with output from the HSM as input into the ESM to produce
alternative disaster simulations. The process can be streamlined to carry out disaster risk
assessment studies or used in setting up disaster management policies.
The DMF is fundamentally anchored on the concepts of individual behavior and
interactions implemented based on the agent-based approach. In general this approach
represents a given system as a virtual society of individuals or agents who follow a set of
rules or heuristics in making decisions in response to changes in their environment or in
interacting with other individuals. Their collective behavior and interactions give rise to
an overall system behavior or phenomena that cannot be deduced from the simple sum of
their individual actions (Bonabeau 2002). The DMF views mass disasters and
emergencies as complex, nonlinear and highly dynamic systems that evolve or emerge
from interactions at the individual level. From this perspective mass disasters and
emergencies cannot be adequately represented by aggregate/empirical functions of
average conditions that obscure the internal dynamics of these phenomena.
Researchers from a wide array of disciplines including ecology, sociology, urban
planning, public health and traffic simulation have successfully applied the agent-based
approach to solve problems deemed extremely challenging to the more traditional
aggregate methods (see Grimm and Railsback 2005, Macy and Willer 2002, Bonabeau
2002, Verburg et al. 2004, Epstein et al. 2002 and Reynolds 1987). Agent-based
modeling provides a more natural and meaningful representation of real systems in
contrast to the abstract high-level empirical approach (Bonabeau 2002). This has practical
implications in terms of validity, applicability and transparency of models. For example
empirical methods used in assessing loss of life from dam failures and flash floods are
mainly based on fitting statistical functions to historical records (Zhai et al. 2006, Brown
and Graham 1988, DeKay and McClelland 1993) with little insight into the low level
processes that could greatly sway the outcome of a given episode in one direction or the
other. Consequently these methods do not provide explanation for the high variability in
the outcome of flood disasters even when their aggregate characteristics, such as size of
population count, percentage of exposure, severance of event are within the same range
(Assaf 2007).
Modeling at the individual level provides a mechanism for normalizing and sharing
information and insight into the disaster phenomena by breaking it into low-level
processes common to other disasters or related phenomena, which can be analyzed and
investigated individually in field or lab work. For example, the lab and analytical results
on the stability of people in moving water (Lind et al. 2004, and Abt et al. 1989) can be
readily incorporated into the DMF. A large body of information on related processes such
as driver behavior, stability of buildings, behavior of people under stress, etc. can be
readily used with minimal reprocessing or interpretation.
The paper presents implementation of the Life Safety Model (LSM), a forerunner of the
DMF, in dam risk studies. The LSM was used to virtually reconstruct the 1959 Malpasset
Dam failure capturing highly detailed and visual simulation of people’s response to
incoming flood wave and the impact it had on them.
White et al. (2001) described how hazard research evolved from two distinct schools.
Geographers view hazards as natural phenomena with a wide range of variations and
implications including positive (environmental) ones. Sociologists on the other hand
considered disaster a social construct and directed their efforts on understanding its
human dimension and enhancing community response and recovery capabilities.
Worldwide concerns over unabated increases in the frequency of hazards and consequent
human and material losses have fueled great interest in hazard research. These renewed
research efforts led to the convergence of the two lines in hazard research into the now
well established vulnerability assessment approach which considers disaster as a
manifestation of a hazard facilitated by intrinsic social and geographic conditions (Adger
2006). The concept of vulnerability has been well received in the hazard research
community and was adopted as a core principle in the Disaster Risk Reduction initiative
by the International Strategy for Disaster Reduction (ISDR) (International Strategy for
Disaster Reduction 2004).
The proliferation of research in the multi-disciplinary vulnerability research has brought
about a long and confusing array of the definitions and interpretations of the concept
(Cutter 1996). Several high level frameworks and conceptual models were introduced to
streamline and formalize the process of vulnerability analysis (Turner et al. 2003).
Brooks (2003) draws a distinction between biophysical vulnerability and social
vulnerability. The latter relates to the inherent characteristics of the human system, e.g.
poverty and social status that make it susceptible to hazards. The former refers to the
potential loss of life and physical damage that could arise from the interaction of hazard
with social vulnerability.
In presenting the hazards-of-place conceptual model, Cutter (1996) viewed the place as
the natural unit for assessing vulnerability as it logically anchors biophysical
vulnerability, defined as the probability of occurrence of hazards at the place, to social
vulnerability, which reflects characteristics of the people residing in the place. Cutter et
al. (2000) applied this conceptual model to assess the overall vulnerability and its spatial
distribution for Georgetown County in South Carolina. They applied Geographic
Information Systems (GIS) technology to compile, analyze and present information on
common hazards and their rate of occurrence in the area, and main socio-economic
indicators of social vulnerability. Their analysis showed significant variability in both
vulnerabilities and in the overall vulnerability. Significantly, social vulnerability did not
always coincide with biophysical vulnerability, which underscores the risk of relying on
biophysical vulnerability as the sole indicator of overall vulnerability. Cutter et al.
recognized however that their approach assumes equal weight for biophysical hazards
and socio-economic indicators, which does not necessarily reflect the actual significance
of one hazard, or socio-economic factor over the other. The logical appeal of the hazardsof-place model and its pragmatic approach based on utilizing readily available
information and technology to seamlessly identify areas of high vulnerability makes it an
ideal tool for emergency management professionals and urban planners.
By assessing vulnerability based on place of “residence”, the hazards-of-place approach
overlooks the mobility of population, a key factor in determining the location of people
when a hazard strikes an area. This is particularly important for rapid onset and intense
hazards, such as chemical spills, earthquakes, flash floods and tornadoes which constitute
most of those considered by the study. Peoples’ pattern of movement follows diurnal,
weekly and seasonal patterns. Consequently, the time of day, week and year is an
important factor in assessing vulnerability. For example, a tsunami that hits a popular
summer resort would result in vastly different outcomes depending on the season it
occurs. The influence of patterns of movement on vulnerability could be accounted for by
considering the rate of occupancy of a place (Assaf et al. 1997).
The temporal dimension of vulnerability is more appropriately represented through
capturing the dynamics of population of the given community. An equally significant and
related issue is the ability to simulate peoples’ behavior and action during a disaster or
mass emergency. These issues were of a prime concern in the efforts at British Columbia
Hydro Corporation (BC Hydro), a large dam owner in Canada, to develop more reliable
method for assessing potential dam breaches on downstream population. In the first phase
of this initiative, the author developed a new conceptual framework for modeling
people’s response to a dam breach flood and its impact on them and buildings. He later
led efforts to implement this modeling approach as the BC Hydro Loss of Life (LOL)
model (Assaf et al. 1997). Although the LOL model determined survivability per
individual as a function of water depth and velocity, it did not represent interaction
among individuals and depicted the environment as one-dimensional with escape routes
perpendicular to channel flow. To overcome these limitations, BC Hydro supported
another phase of activities to develop the 2-dimensional Life Safety Model (LSM) (Assaf
and Hartford (2002), Assaf (2002) and (Assaf and Hartford (2001)). The LSM integrates
output from a flood simulation model with virtual world made up of individual entities
representing people, buildings, vehicles and roads.
The DMF evolved from the LSM to address a wider range of mass disasters. While the
LSM is fundamentally a support tool for dam risk assessment, the DMF provides a
generic platform to capture the dynamics of different types of mass disasters in support of
risk assessment and emergency management. Despite being an earlier product, the LSM
in retrospect represents an implementation of the DMF architecture for dam safety
studies. Consequently, examples of how the DMF could be implemented are based on the
LSM. The paper presents the architecture and main components of the DMF and
illustrates its application in dam safety risk assessment.
Review of disaster/emergency models
The issue of mass emergencies and disasters has attracted great interest from researchers
in a wide range of disciplines such as dam safety, risk analysis, hydrology, seismology,
traffic modeling, and building safety. Multitude of models and procedures were
developed to address problems ranging from risk assessment, emergency preparedness,
and evacuation planning. Table (1) presents an overview of models reported in the
literature classified according to their purpose and approach into loss-of-life empirical
formula, building evacuation models, traffic evacuation models, population models, flood
evacuation models and loss assessment models.
Earlier procedures were empirically based and consisted mostly of formulas that estimate
overall loss of life and physical damages as functions of global variables such as total
exposed population and property, and parameters that relate to warning and intensity of
the hazard. For example DeKay and McClelland (1993) used historical events to develop
a formula expressing loss of life from floods and dam breaches as a function of total
population, average warning time and lethality of flood wave. Zahi et al. (2006)
developed a formula for estimating loss of life from floods in Japan based on the number
of houses inundated and type of flood, whether a flash flood or a rainy season one. Badal
et al. (2005) estimated number of earthquake casualties in Spain as a function of
population density and magnitude of earthquake. These empirical formulas are still
widely used due to their simplicity. However, they fail to capture the spatial variability of
bio-physical and social vulnerabilities, raising concerns about their use in dam risk
assessment (Hartford and Kartha 1995).
Challenges in planning emergency evacuation from large and complex buildings have
motivated the development of several building evacuation simulation models. The SAFER model represents the indoor environment of rooms, stairways and hallways as a
network of nodes, with people movement simulated as flow between these nodes (Gupta
and Yadav 2004). The model applies networks optimization methods to determine
optimal evacuation paths. The EGRESS model represents the building environment as a
grid, with people and obstacles as grid points (Santos and Aguirre 2004.) Grid points
representing people move with variable speeds towards exits. The more advanced
BuildingEXODUS model represents people as agents evacuating and interacting based on
a set of rules (Gwynne et al. 2005). By design, building evacuation models are restricted
in their application to enclosed environments and are not readily applicable to simulate
natural, open-space or spatially extensive disasters.
Emergency management could involve mass evacuation of communities in anticipation
of natural hazards such as wildfires and hurricanes. Cova and Johnson (2002) have
leveraged the sophisticated capabilities of Paramics, a leading traffic simulation
commercial software, to simulate evacuation of a small community exposed to the risk of
wildfires. Paramics uses agent-technology to simulate drivers’ behavior and traffic flow.
Cova and Johnson used a statistical trip generator to produce input for Paramics and
develop a set of optimal evacuation scenarios. However they do not offer a clear
guidance on carrying out multi-scenario analysis or selecting values for the statistical
Reliable estimation of population is necessary to carry out risk assessment studies and
emergency management. In support of these and other activities including security,
public health and energy requirement studies, the U.S. Department of Energy’s (USDOE)
Oak Ridge National Laboratory (ORNL) has developed LandScan to estimate population
worldwide and USA wide (at 3 arc-second resolution) using census data, GIS and remote
sensing based on key indicators including land use, elevation, and proximity to
transportation, and places of activities. The model provides both nighttime and baseline
daytime population estimates. The first estimate represents resident population, while the
later compromises student and worker populations (Bhaduri et al. 2007).
In response to major concerns over nation-wide impact of earthquakes on buildings and
infrastructure, the US Federal Emergency Management Agency (FEMA) sponsored
several studies that led to the development of HAZUS in 1997 as an earthquake loss
assessment software tool. Over the following few years FEMA significantly restructured
and repositioned the software as a multi-hazard integrated assessment system. Renamed
HAZUS-MH (MH refers to multi-hazard) the new system contains an extensive GIS
database on buildings and census information and supports a consistent set of tools to
assess potential damage from three main hazards: earthquake, hurricanes and floods
(Schneider and Schauer, 2006). The impact assessment can be conducted at three levels
of ascending complexity and detail of analysis. The first level involves using information
from a default database to produce a low cost preliminary loss assessment. Highest level
requires using more detailed local data and relatively more sophisticated tools. HAZUSMH calculates loss of life only for earthquakes, and not for hurricanes or floods. This is
possibly since HAZUS-MH estimates loss of life as function of damage to buildings.
Earthquakes occur with no warning and consequently do not trigger a pre-evacuation,
making damages to buildings a good indicator of casualties. In contrast hurricanes and
floods develop over hours or days giving population the opportunity to evacuate albeit at
different timings and speeds. This makes loss of life less or inconsistently associated with
the level of damage to buildings occupied prior to the hazard onset.
Each of the applications discussed above handles one or two aspects of assessing the
consequences of a mass emergency or disaster. The DMF integrates several modules to
represent collectively the key processes that give rise to a disaster. A population module
represents the society as a set of individuals with characteristics (age, gender and physical
mobility), and links to other individuals, places of activities, vehicles and roads that
influence their behavior and interaction as they become aware and make decisions
regarding evacuation. A hazard module provides the spatio-temporal simulation of the
hazard medium. Inputs from the two modules are then integrated into a simulator that
tracks movement and fate of each individual throughout the disaster episode.
Architecture of the Disaster Modeling Framework
The DMF provides a generic, flexible and intuitive virtual environment to examine the
social as well as the physical aspects of a mass disaster and estimate loss of life. Drawing
upon experience in earlier modeling efforts to assess loss of life from potential dam
failures and realizing the great similarities among disasters regardless of their instigating
hazards (Scawthorn et al. 2006) the author developed a more holistic and generic
approach to modeling mass disasters as articulated in the high-level architecture of the
DMF shown in Figure 1.
The DMF couples two modules, the Community Simulation Model (CSM) and the
Hazard Simulation Model (HSM), representing the potentially affected community and
the hazard, respectively, into a geo-referenced Evacuation Simulation Model (ESM) that
tracks the unfolding of a potential disaster at the individual levels of persons, families and
other groups, buildings, places of activities, vehicles and infrastructure. The simulation
produces individual-based information (numbers, locations, socio-demographic
characteristics, escape attempt tracks, reception and compliance of warning and physical
conditions) on the potentially affected, injured and killed populations. Simulation output
also contains information on damages to buildings and infrastructure. A GIS based
database stores simulation results to facilitate data quality control, analysis and
presentation using common GIS tools. A central Framework Manager Module (FMM)
coordinates processing and interactions among different modules.
There are two setup levels for the DMF. At the low level, specialized users in hazard and
population modeling set up the HSM and CSM, respectively. At the high level, users
such as policy makers, risk assessment and emergency management professionals
formulate scenarios by defining time of day, week and year and level of hazard. Users
can then process these scenarios to create input files into the ESM, which in turn
simulates the disaster event.
A DMF scenario represents a unique combination of certain community and hazard static
and dynamic conditions and disaster mitigation policies. In dam risk studies, for example,
analysts formulate several scenarios, each representing a unique combination of water
levels in the reservoir, which determine the scale of the flooding event, and the time of
day, week and year of failure which dictates the occupancy density and level of alertness.
Figure 2 shows the scenario tree configured for a dam risk study showing the outcome of
each scenario in terms of the Population at Risk (PAR), estimated Loss of Life (LOL) and
corresponding probability of occurrence.
The DMF provides flexibility in the choice of the community and hazard simulation
models as long as their outputs can be processed to be compatible with the text-based
format of the ESM. This equally applies to the selection of the visualization and analysis
tools. The main modules of the DMF are discussed in the following sections.
The Community Simulation Model (CSM)
Despite its richness and long history, the literature on addressing the human aspect of
disasters is lacking in providing explicit and quantitative representation of potentially
affected communities necessary for objective, transparent and reliable analysis and
management of disasters. The few modeling efforts reported in the literature are
empirical and anecdotal in nature and generally address single processes only
(Blanchard-Boehm 1998; Gillespie and Murty 1991; Gladwin et al. 2001; Kirschenbaum
1992; Perry 1989; Perry and Lindell 1991; Sorensen 1991).
The CSM utilizes information from different sources to create a virtual world of
individuals each with a set of characteristics that define its behavior, interactions with
other individuals, movement and vulnerability to the hazard medium. The CSM defines a
set of classes representing different types of people, e.g. a working man with family, male
student, working women, single mother, senior, disabled, toddler, etc. These classes act
as master copies from which individuals are instantiated. People classes contain
statistical parameters from which individual characteristics are sampled. The CSM uses
census, property, survey and other data to determine distribution of classes among the
potentially affected population. The class attributes and their statistical parameters are
derived from information in the literature or could be determined via dedicated research.
For example, Assaf and Hartford (2002) used findings from studies on human stability in
floodwater (Lind et al. 2004, RESCDAM 2000, and Abt et al. 1989) to calculate
statistical means of toppling water depths and velocity for men and women. They in turn
used them to statistically generate these parameters for the virtual individuals. Findings
from RESCDAM (2000) were used to generate combinations of water depth and
velocities that would topple building of wooden structures.
Detailed analysis of past mass emergencies or disasters including Malpasset (Snell and
Smith 1959, and French Ministry of Sustainable Development 2009) and Teton
(McDonald, 2006) dam failures using technical, documentaries, media-based as well as
anecdotal information provides valuable insight on circumstances and decisions and
actions made by people that have significant impact on the course of events at the
individual or global level. For example, the decision to return to the hazard after a
warning was the principle reason for most deaths in the Teton dam failure. Assaf et al.
(1997) represented this behavior as a parameter in the BC Hydro LOL model that will
trigger the return of some individuals to the site to pick up belongings after a long
warning without occurrence of the hazard.
The approach here emphasizes learning from detailed accounts of individual actions and
events within the larger disaster event to develop better estimates of rules of behavior and
survivability factors. As presented in a later section, the approach has been successfully
validated and applied in dam breach studies, and is proposed here for application for
other disasters and mass emergencies.
The CSM Design
The CSM portrays a community from two perspectives: a persistent Static View and a
Snapshot. The former represents attributes and objects that are permanent or relatively
stable during a disaster assessment study including people’s age, ethnic background,
physical characteristics, family ties, places of residence, work and study, buildings,
infrastructure and vehicles. Generated from the former, the latter is analogous to an
image captured of the community at a given moment showing where and with who each
individual is situated, his/her sate of alertness (awake, asleep), activity (working, driving,
walking, etc), and reception and compliance of warnings. In a typical operation of the
CSM, the analyst configures a single static view and then uses it to generate several
snapshots representing potential settings of the community at the onset of the disaster. In
assessing individual extreme events, such as an earthquake during a busy tourist season
or a midnight flash flood in a residential area, the analyst runs a snapshot representing the
given time of day, week and year along with an HSM-simulated hazard event into the
ESM to produce a simulation of the potential disaster event. In disaster risk assessment
studies, the analyst may set up a batch run to generate a set of snapshots representing the
full spectrum of the year and run them in the ESM in different combinations with a set of
HSM-simulated hazard events. Each disaster scenario generated by a pair of a given
snapshot and a given hazard event has a probability of occurrence calculated equal to the
product of the frequency of the time of the year of the snapshot and the probability of
occurrence of the hazard event. The DMF streamline this assessment process as shown in
Figure 3.
The CSM represents a potentially affected population as a virtual community of
individuals, their families, neighbors, work groups and their associated places of
activities, buildings, vehicles, road network and open spaces. Figure 4 illustrates the
topology of the CSM community. The CSM uniquely identifies each person in the
community and assigns him/her attributes that describe his/her family ties, age, gender,
occupation, ethnic background, education, preparedness, mobility, and physical
characteristics. The CSM passes on this information to the ESM which uses them to
simulate a given person’s behavior and determine his/her survivability in encountering a
given hazard medium. The CSM places special focus on the family unit since familial
behavior highly influences individual behavior and consequently plays a key role in
disasters, particularly with respect to preparedness, reception and compliance of warning
and evacuation (Hultåker 1983; Kirschenbaum 2006; Carter et al. 1983; Clason 1983).
For example, the CSM assigns parents links to their children, used by the ESM to prevent
family members from evacuating independently.
The CSM adopts a geo-referenced spatial system to determine locations of individuals via
their associations with three classes of spatial objects: buildings, open spaces and road
networks. Individuals are not hard-linked to specific locations. For a selected time of the
year, week and year, the CSM assigns on the fly individuals to specific locations based
on their patterns of activity as represented by the Whereabouts schedule and their
associated places of activities. The Whereabouts schedule specifies the likelihoods of the
person being engaged in specific activities including working, staying at home, driving,
etc. Typically during the setup of the static view the CSM assigns a person a whereabouts
schedule from a set of whereabouts schedule templates defined based on identified
categories of people including working person, household parent, school children, etc.
For example, Table 2 depicts a whereabouts schedule for a working person. The schedule
categorizes activities seasonally, weekly and daily in addition to those during holiday
times. For a given snapshot a statistical generator determines based on the Whereabouts
likelihoods the activity the person is engaged in, which consequently identifies the place
of activity that is either associated with a building, an open space, an outside the area, or
a section of the road network if the person is driving or commuting.
The buildings are important elements in the CSM since they contain the space where
people spend most their time in an average year. Not only they provide safe or perceived
safe havens for people, but they are also at risk of getting destroyed or failing to protect
their inhabitants during disasters. The building object contains several attributes used in
determining a building’s stability including type of material, age, height and construction
standards. The building act as a container to the place of activity object, which links
people to buildings as described above. The place of activity concept provides flexibility
in developing representation of complex multi-use built environments such as business
complexes and shopping centers.
Mobility of population at risk - in terms of presence of drivers and access to vehicles - is
critical to the success of evacuation efforts (Cova 2005). This is particularly important for
events such as wildfires and hurricanes where a window of few hours could be available
to evacuate people. In many dam breaches cases also, several hours to days are available
for evacuation as breach develops, flood wave travels toward sites situated far
downstream, or if a good detection and warning system is available. This modernly
afforded mobility can however act as a double edge sword. French et al. (1983) found
that 43% of 190 deaths reported in 16 flash floods in the US are vehicle related. Drobot et
al. (2007) indicated that over half of the death in floods involved vehicles. Vehicles
provide people with a fast way out of a danger zone, yet unmanaged vehicular evacuation
could lead to massive traffic jams that may seriously hamper evacuation efforts. Rohde
(2002) indicated that evacuees flow and emergency force during the Laguna Hills Fire in
California were locked up in serious traffic jams that have exacerbated this catastrophe.
The CSM represents vehicles as objects with attributes describing their capacity and
structural stability, and assign them to individuals based on information on car
ownership. The CSM models road networks as collections of topologically connected
spatial linear elements and nodes. The ESM uses road networks to simulate traffic
movement and evacuation.
Generation of a Static View
In implementation, the static view is a GIS database composed of a set of interrelated
tables each maps to a corresponding object as conceptually represented in Figure 4.
Relationships among tables map to corresponding relationships among the conceptual
objects. The cost and effort required to populate a static view database vary depending on
the population density, land use and size of the area under consideration and the scope
and objective of the DMF study. For example, a small (2 to 3 people) investigation team
may require one to two weeks to develop a static view database for a small community of
up to a 100 dwellings, such as a trailer community in a wildfire-prone area, or a small
Tibetan village exposed to the risk of snow avalanches. In contrast, large urban areas may
require several experts including some with solid GIS experience working for several
months to collect compile and corroborate information from several sources that may
include census, cadastral and municipal data. In the former case, the study team can
manually enter information in the GIS database on the locations and characteristics of
buildings, characteristics of residents (age, sex, etc.) and mobility. In the later case, the
size, heterogeneity and the varied quality of information may complicate the process of
developing the static view. To overcome these problems, Assaf and Hartford (2002)
developed an iterative semi-automated routine to compile census and cadastral data in
addition to field surveys and other locally available data to produce a static view of a
given urban area. They applied this routine in a BC Hydro dam safety study of the
Keenleyside dam located few kilometers from the City of Castlegar, B.C. The study
involved assessing the seismic risk to the dam and its implications to downstream
population in Castlegar through investigating several alternative dam failure scenarios.
Assaf and Hartford developed the static view routine to process Canadian census data,
however the routine can be modified to handle census data of different format given that
it contains similar type of information. Statistics Canada is responsible for collecting,
compiling and disseminating census data in Canada. Statistics Canada collects census
data per individual household for most of the population that covers a wide range of
inquiries. However, to maintain privacy, Statistics Canada publishes census information
aggregated as demographic statistics per enumeration areas (EA), each made up of few to
several hundreds of households. The Canadian EA statistics include type of residential
groups (e.g. Family with children, single mother, etc.), their size, age and gender
structure (number of children and age categories, senior citizens, etc) in addition to other
information (Statistics Canada 1996) (see the top table in Figure 5). A typical cadastral
data in Canada includes use type of buildings, their construction type, age and physical
condition as shown in the sample table in the second row from the top in Figure 5. The
cadastral data includes an extensive array of items which are not considered here.
The static view routine includes several steps. For a given community, the routine
generates the static view as described below with references to the example presented in
Figure 5.
For each EA the routine generates residential groups with size, gender and age
structure that match those of the census demographics for the given EA. To do this the
routine first uses a statistical random generator to create the same number and type of
residential groups in the census data, which readily determine the number and gender of
parents and seniors. The routine then calculates the number and age groups of children
through several (3 to 5) iterations to maintain consistency between the total number and
age group of children and the family structure demographics. The routine then assigns
each residential group a unique ID and stores it in a table that also shows it’s associated
EA and the number of individuals in each category (man, woman, adult (unknown
gender), senior and children of different age groups). As an example, the routine used the
demographics of the EA #59011160 shown in the upper table in Figure 5 to generate over
200 residential groups. Five of these groups appear in the third table from the top, which
is a partial display of the complete Residential Groups table. The highlighted residential
group #3177 is a family-with-children type that has a father, mother, a son or daughter 18
to 24 years of age and a child less than 6 years old. At this stage, the routine has
associated each residential group with a specific EA, but left the determination of its
exact location to a later step.
Using cadastral data that includes information on the type and use of the
buildings, the routine identifies the specific locations where different activities take place
including residences, schools, markets, hospitals, etc. It then stores this information in
corresponding tables representing places of residence, work, school, shopping,
recreational. For example, the routine used the information on the apartment building
with ID #2855 in the middle table to the right (Figure 5) to generate several apartments as
places of residence. Five of these apartments appear in the middle table to the left, which
is a partial display of the Places of Residence table. The routine then used spatial
processing to identify the EAs where the buildings are located.
The routine assigns the residential groups to specific places of residences located
within the same EA through an automated matching process that corroborates
information from the two independent sources of information of census and cadastral
data. For example, the routine only assigns senior residential groups to places of
residences classified as senior residences. It also gives family residential groups higher
priority for being assigned to detached homes. In the example presented in Figure 5, the
residential group #3177 resides in apartment #4532 located in building #2855.
An analyst quality checks the generated GIS database for inconsistencies such as
placing residential groups disproportionally in certain parts of a given EA or locating
large number of children per several families in an area known to have a low count of
children per family. The analyst may modify the database manually or rerun one or more
of the first three steps few times to obtain a logically consistent representation of the
region. The analyst may also manually modify the database to corroborate additional
information from field surveys, eyewitness observations and other sources. In particular,
this approach facilitates incorporating higher quality data to provide more detailed
representation of highly significant areas or hotspots such as those closer to the hazard
medium or those that house highly vulnerable segments of the populations such as
children or the elderly, e.g. schools, hospitals and senior residences.
The routine uses information on residential groups to instantiate individual
persons, where each person is described in terms of his/her age, gender, and family links
to other members in the group (father, mother, son/daughter). In the example shown in
Figure 5 the routine instantiated the following four individuals for the residential group
(#3177): a man (#7884), woman (#7885), a son or daughter aged 18 to 24 years (#7886)
and a child less than 6 years (#7887). The routine uses the age and gender of a given
individual to assign him/her characteristics that describe his/her behavior and
survivability in a disaster. These characteristics include those that are generally common
for different types of hazard such as interpretation and compliance of warning, risk taking
attitude and mobility. Other characteristics are hazard dependant and include for example
the toppling depth and velocity of flood waters (Assaf and Harford 2002). In general
behavior and survivability in disasters is highly associated with the person’s demographic
category. For example young men are generally more risk taking, agile and more capable
in surviving contact with a hazard medium in comparison to young children and elderly
citizens who are the most exposed and vulnerable groups in disasters. It is estimated, for
example, that third of the Indian Ocean Tsunami victims were children (Penrose and
Takaki 2006).
The routine utilizes Information on schools and colleges records, labor records
and additional census data to assign individuals categories as dependent, students,
employed, housewives, and unemployed. The routine then assigns each person based on
his/her employment status, attendance of school, and dependency, a whereabouts table
that reflects his/her pattern of activity. The routine selects the whereabouts table from a
set of templates configured for different types of persons including full-time workers,
home staying parent, and students.
Lastly, the routine uses information on vehicle ownership and traffic intensity
patterns to assign vehicles to residential groups and persons. It also assigns vehicles
characteristics that determine their vulnerability to the given hazard medium. For
example vehicles are reported to be less stable in flood water in comparison to people
(Walsh and Benning 1998).
The static view database of Castlegar study contained approximately 9,000 people, 3,800
residential groups and 3,500 buildings. BC Hydro team of 5 experts spent about 6 months
to develop this database. However, a large share of the effort went into developing and
testing the automation routine.
Through merging this otherwise disparate information, the static view database provides
a new insight into the community’s state of readiness and vulnerability to mass hazards.
It could also highlight critical hotspots that would have been missed by following a more
traditional approach of data gathering. For example, in the Keenleyside dam safety study,
the automation routine highlighted based on the census data the presence of a significant
number of senior citizens in a specific EA and linked them to a large senior apartment
building residence situated very critically in the 100-year flood zone. A field
investigation team confirmed this information and further incorporated additional field
information on the exact number of suites and some of the building’s structural
characteristics such as its height, construction material, etc.
Given that it relies mainly on aggregated data, the above procedure is not expected to
produce a virtual world of objects that match their corresponding real entities on a one-toone basis. It does however produce a virtual world with demographics that matches the
real one. That also holds true to two static views created via independent runs. In fact an
infinite number of different, yet demographically similar, static views can be created
through this process. Running these static views through the remaining procedures of the
DMF over a wide spectrum of conditions would produce different results. Sensitivity
runs should be conducted where several static views are run through the model to assess
the expected range of outcome.
Generation of Snapshots
A configured static view contains the necessary information to generate snapshot views
of the community at selected times of the day, week and year. The generation process
involves randomly placing each person in the community at a place of activity based on
the probabilities specified in the Whereabouts table. Using Table 1 for example, there is
84% chance that the person will be located at his/her place of work during daytime hours
between 8 am and 5 pm. In contrast, there is only 5% chance that he/she is on the road
during the same period. The snapshot view also captures several attributes that relate to
the readiness of a given individual to the hazard including the state of alertness (sleep or
awake), access to vehicles, proximity to independent/dependent individuals. The CSM
explicitly considers this latter attribute through the group object which ties individuals
through family or work links. For example, the CSM model parents to maintain
proximity to their children during a crisis, even if their chances of survival were to
improve if they act individually. The CSM can also capture group behavior under nonfamily situations such as the tendency of people to help strangers in a mass emergency
(Goltz 1984).
The snapshot view also contains attributes related to the group or individual warning
compliance and evacuation decision making process during crisis. For example, people
vary in their responses to the threat of floods in terms of evacuating buildings, delaying
evacuation or returning to get belongings, driving through running water (a common
cause of death), and evacuating by vehicles or on foot. The CSM assigns these behaviors
to individuals and groups based on statistical values obtained from established disaster
research (e.g. Kirschenbaum 1992).
The Hazard Simulation Model
The DMF approach calls for leveraging existing hazard simulation models by projecting
their output onto the virtual disaster simulation environment. As such an HSM can be
chosen from established hazard-specific simulation models. In the case of flooding
disasters, HSM can be TELEMAC or MIKE 11 which are leading flood simulation
models. The DMF processes output from the HSM to produce estimates of key variables
at the grid nodes of the ESM. Key variables vary by the type of hazard and could include
wind speed, water depth and velocity, and chemical spill plume concentration and
The upstream position of the HSM in the DMF modular architecture indicates that the
HSM is not required to process feedback from the CSM or ESM. This design
arrangement provides flexibility in the choice of the HSM, and reduces the need to
develop, usually at very high cost, an in-house HSM or significantly customize an
external one.
The Evacuation Simulation Model
The ESM simulates the response of people to an impending mass hazard and the outcome
when they and the main elements of their environment come in contact with the hazard
medium. The ESM superimposes the HSM output on a geo-referenced environment it
creates based on the given CSM snapshot view. It then determines the outcome of the
hazard/community contact at each grid point per given simulation time step based on the
values of superimposed hazard variables and characteristics of community objects. The
grid points could be spaced irregularly so that more grid points are used in areas where
higher details are required and vice versa.
The ESM incorporates several modules to capture the dynamics of a disaster. It tracks
each person individually or as a member of a group throughout the disaster event. The
person receives warning, responds to it by evacuating by foot or in vehicle or staying put,
may survive the event without encountering the hazard medium or may come in contact
with it, survives with no injury, or gets injured or killed. The ESM simulates these
processes via a central modular algorithm as shown in Figure 6, where each process is
represented as a block in the algorithm flow chart.
The first block in Figure 6 represents reception of warning through formal and informal
means including automatic warning systems, police, media, other people and physical
observation. The ESM emulates the warning process as a grid of interconnected virtual
warning centers distributed throughout the impacted area. The DMF analyst can set up
one or more warning center(s) to become active on a given time or get activated once it
comes within certain distance from the hazard medium. Once triggered a warning center
initiates a warning which it passes on with specified delay and rate of dissemination,
reflecting the reliability and efficiency of the warning system, to people and other
warning centers. A person can also initiate a warning once he/she becomes aware through
own observation or via information by other people or warning centers. This parallel
propagation of warning mimics real life warning where people become aware of the
hazard through several interdependent sources such police warnings, telephone calls by
relatives and friends, radio and TV broadcasts, or own sensing of the hazard or signs of
its impending arrival, such as smoke of wild fire or the sight of a hurricane from a
The following decision block represents the response to a warning, which the ESM
implements as a person/group behavioral and mobility process that involves the decision
to evacuate or stay in place and delay evacuation to overcome shock, get ready, assist or
rescue other family members, and collect belongings. The ESM determines how
individuals behave by assigning them logical tags that relate to their perception of
protection provided by the building, personal risk taking attitudes, and confidence of the
warning. The ESM assign these tags based on statistical means derived from disaster
literature. For example, family links take precedence over decision to evacuate, where
parents are modeled to delay evacuation until getting all family members ready to
evacuate. This is a particularly important aspect of disaster social behavior that has been
strongly supported by disaster research (Kirschenbaum 2006).
Depending on the reception of warning and the outcome of the decision to evacuate, a
person/group has three alternatives to consider: staying in the building, or evacuating on
foot or by vehicle. For those who were already outside buildings either walking or on the
road at the onset of the disaster will follow the corresponding logic shown for pedestrians
and those in vehicles.
The outcome of not evacuating a given building depends on whether the building would
come in contact with the hazard medium, and if so whether it can protect its occupants
from the hazard effect. This depends on the nature of the hazard and the building’s
structural characteristics. Tall and well constructed concrete buildings are considered
resilient to floods especially if occupants manage to escape to the upper floors of the
building. Light wood buildings are considered more ideal in earthquake disasters. The
ESM represents these characteristics as parameters passed on from the CSM. These
parameters could include age of the structure, construction material, number of floors,
etc. The ESM processes these parameters and the hazard variables at a given building’s
location into a hazard-specific module to determine the state of the building throughout
the simulation run. For example, the ESM uses the height of the building and
construction material along with the flood depth and velocity to estimate the damage to
the building during a flood disaster simulation run.
Individuals who choose to evacuate a building are faced with the decision to select a
method of evacuation and a destination. Assaf and Hartford (2001) have introduced the
concept of a Perceived Safe Haven (PSH) to represent potential evacuation destinations
that are deemed, and may not be necessarily, safe by a given person/group. Depending on
the nature of the hazard, preparedness level and type of transportation, the ESM assigns
each person/group a set of PSHs, which could be shared with other persons/groups.
Spatially, the PSH can be a point, a line or an area. For example, the DMF analyst can
designate a 200-year flood contour line as a PSH. The PSH concept offers the analyst
flexibility in representing unwarranted evacuation schemes or situations where the
disaster outcome got worse as people evacuated to unexpectedly unsafe locations.
Several factors influence a person’s choice of the method of evacuation including the
nature of the hazard, the location of the PSH, availability of vehicle and condition of the
traffic network. For example, an effective method to avoid being swamped by a flash
flood is to climb the river valley wall by foot rather than driving a car along a river road.
The ESM includes a pedestrian and a vehicular transportation networks to simulate both
evacuation by foot and vehicle. A person/group evacuating by foot will move with a
speed calculated initially based on physical characteristics tied to gender and age
information obtained via the CSM. The ESM may modify the speed of a certain group to
reflect the presence of physically less capable members such as children. The model
assesses the status of the person as he/she comes in contact with the hazard medium
based on the hazard’s variables and the person’s characteristics. In the case of floods, the
ESM uses water depth and velocity along with age and sex of the person to estimate
his/her chances of being drowned (Assaf and Hartford 2002).
Evacuation by vehicle differs in important aspects from that on foot. Vehicles are much
faster and may provide additional protection, yet their movement is dependent on the
functionality of the transportation system, which could be greatly impacted by traffic
jams and potential disruption, e.g. due to flooding, or destruction, e.g. due to earthquakes,
of the road network. These are important issues in emergency and disaster management,
where uncoordinated efforts could lead to unwarranted all-out evacuation creating traffic
jams that impedes the evacuation of truly impacted areas.
The ESM includes a fully integrated individual-based traffic simulation model, which
incorporates basic driver behavior including car following, collision avoidance and gap
acceptance. The ESM models vehicles as individual entity with characteristics that
determine their capacity and resilience to hazards. It models the traffic infrastructure as
networks of road segments and nodes - created from GIS road network data or prepared
or edited manually - that determine road connectivity, capacity and flow direction.
It is important to emphasize the importance of separating the functionalities and the weak
coupling among the three DMF core models. The ESM acts as a simulation engine that
expects information on the state of both the community and hazard from the CSM and
HSM. Consequently the ESM is not restricted to a particular CSM or HSM. In fact input
to ESM can be edited and prepared manually giving great flexibility to use in a variety of
case studies. For example, the DMF analyst can choose to manually prepare the ESM for
small scale disasters involving remote communities or villages, where the data can be
efficiently field collected and processed in a timely fashion.
Demonstration of the Potential of the DMF as a virtual disaster
simulation environment
As a demonstration of the potential of DMF in simulating disaster events particularly at
the individual level, this section presents a brief description of an extensive forensic
analysis of the Malpasset Dam failure disaster to validate the LSM. An investigation team
from BC Hydro and a consulting firm conducted this validation study over several
months where it examined a large set of information sources including official French
national census, cadastral and mapping data, published documents, newspapers and
magazines, video documentaries, photos, interviews and exchange with experts and
eyewitnesses (Johnstone et al. 2003). The team developed a GIS database to store and
link these pieces of information. The database provided input data to the LSM and
reference information to assess its simulation of actual events.
At the time of its completion in 1955, the Malpasset concrete dam was a symbol of
France’s technical edge in developing aesthetically appealing and structurally sound
dams. The exceptionally thin double curvature dam was dubbed as “a beautiful, shapely
lady - very daring and very delicate” (Snell and Smith 1959). The dam reservoir provided
water supply and irrigation to the downstream Reyran river valley and city of Frejus on
the French Riviera. It took 4 years till the dam was first filled at the end of 1959. Due to
an oversight in geotechnical analysis of the bedrock, the dam foundation was not
adequately designed and gave way under the immense momentum and torque of the 66.5
m-high water mass. A 40 m high wave moving initially at 70 km/hr rushed through the
steep narrow valley washing everything in its way including debris, buildings, trees,
vehicles and human bodies (Snell and Smith 1959). Large several-ton concrete pieces
torn from the dam core were found strewn few hundred meters downstream of the dam. It
took 25 minutes for the wave to travel 12 kms to reach Frejus and finally emptying into
the Mediterranean 16 kms downstream of the dam leaving several hundred (estimates
range from 423 to over 550) people dead and destroying over 150 buildings in addition to
roads, railways and crops. Close to 7,000 people were believed to be affected by the
disaster (Johnstone et al. 2003).
To represent the flood wave characteristics the validation study relied on a well calibrated
hydrodynamic simulation of the Malpasset Dam breach produced by the Laboratoire
National d’Hydraulique (LNH) of the Electricité de France (EDF) using the TELEMAC2D model (Hervouet 2000a and Hervouet 2000b). The LSM estimated loss of life from
the Malpasset dam failure to range from 424 to 514 which is well within the reported
values of 423 and 550 (Johnstone et al. 2005). The model was also able to virtually
replicate detailed individual events including the escape of the dam operator with his wife
to a high ground nearby after hearing loud cracking sounds very shortly before the
collapse of the dam. It also captured the sudden death of 30 highway construction
workers camped few hundred meters downstream of the dam (Snell and Smith 1959).
Four computer screen snapshots of the Malpasset event simulation are presented in
Figure 7 to illustrate the dynamic capabilities of the LSM environment. The figure shows
the progression of the Malpasset dam breach disaster in an area located 1 kilometer
downstream of the dam. The upper left image shows the flood wave approaching the area
at 1:50 minutes after the dam breach, with some residents groups (labeled 1, 2, 3, 4 and
5) already evacuating the area to a higher area via a bridge spanning the river. The upper
right image shows the situation 10 seconds later, with the flood wave only few meters
away from some residences. The lengths and directions of flow vectors indicate the
magnitudes and the directions of corresponding flows, respectively. Resident groups #1,
#3, #4 and #5 have moved northwest towards the safe area, with resident #2, who
happened to be pedestrian, lagging behind. 10 seconds later, the flood wave has
overcome resident groups #10, #11, #14 and #15 as shown in the lower left image.
Residents #12 and #13 have moved south towards the bridge. Another resident (#18) is
shown moving in from the south towards the bridge. The last image shows the flood
wave moving swiftly to the south, sweeping away additional residents (#6, #7, #8 and #9)
and closing in on residents #2, #12, #13 and #18. These simulation results constitute a
portion of 1.5 hour ½ second-step simulation run that covers the whole area from the dam
site to the river outlet into the Mediterranean.
Summary and Conclusions
The Disaster Modeling Framework (DMF) offers a novel approach to capture the highly
complex, dynamic and spatially variable human and physical aspects of disasters. Its
flexible architecture integrates the individual-based Community Simulation Model
(CSM) with the physical Hazard Simulation Model (HSM) to simulate a combined
human/hazard scenario through the visual environment of the Evacuation Simulation
Model (ESM). The CSM untangles the highly intertwined dynamic and static aspects of a
human society into the snapshot-static views pattern to facilitate creating logically
coherent and consistent scenarios. The ESM utilizes Agent based approach to simulate
people’s behavior, interaction and movement as they become aware of an impending
hazard or come in contact with its medium.
It is important to emphasize that the objective of the DMF is not to predict but rather to
provide a range of potential outcomes of mass disasters under given conditions. The
aftermath of a historic disaster represents one realization of the ensemble of potential
outcomes that the disaster could have turned into. Calibrating the DMF should not lean
heavily on matching the outcomes of real disasters but rather to gain insight into the
processes that led to these outcomes.
Although the DMF was designed to provide a one-stop and comprehensive environment
to assess and plan for disasters and emergencies, its architecture is flexible to integrate
with other existing systems. In particular, HAZUS-MH with its vast database and
standardized toolset can provide valuable input to populate and calibrate the DMF
components. It can also be used as a prescreening tool to identify and prioritize high risk
The DMF constitutes an important step towards a more integrated modeling approach to
analyze and manage mass disasters. It also has the potential of providing a virtual
environment for analyzing the impact of risk perception and social factors on loss of life.
It should be noted however that since the DMF has not been verified for applications
other than those of dam breach studies, the framework is still considered in the
experimental stage with respect to modeling other hazards.
The author thanks the editor and the three anonymous reviewers for their valued
comments and suggestions which have considerably improved the quality of the paper. It
should be noted that the first version of this manuscript was submitted for publication on
November, 2006.
Abt, S. R., Wittler, R. J., Taylor, A., and Love, D. J. (1989). “Human Stability in a High
Hazard Flood Zone.” JAWRA Journal of the American Water Resources Association,
25(4), 881-890.
Adger, W.N. (2006). “Vulnerability.” Global Environmental Change, 16(3), 268-281.
Assaf, H. (2007). “Discussion –"An Empirical Model of Fatalities and Injuries Due to
Floods in Japan" by Guofang Zhai, Teruki Fukuzono, and Saburo Ikeda.” JAWRA
Journal of the American Water Resources Association, 43(5), 1344-1346.
Assaf, H. (2002). “Understanding dam emergencies and formulating plans through virtual
reality modeling.” EAP 2002 International Workshop for Emergency Preparedness at
Dams, sponsored by FERC and ASDSO, Niagara Falls, USA.
Assaf, H. and Hartford, D. N. D. (2002). “A virtual reality approach to public protection
and emergency preparedness planning in dam safety analysis.” Proc., Canadian Dam
Association (CDA) 2002 Annual Conference, CDA, Victoria, Canada.
Assaf, H., and Hartford, D.N.D. (2001). “Physically-based Modeling of Life Safety
Considerations in Water Resource Decision-Making.” Proc., The ASCE World Water and
Environmental Resources Congress, Orlando, Florida.
Assaf, H., Hartford, D. N. D, and Cattanach, J. D. (1997). “Estimating Dam Breach Flood
Survival Probabilities”. Proceedings of the Australian Committee on Large Dams
(ANCOLD) Conference on Dams, Perth & Kununurra, Australia.
Badal, J., Vazquez-Prada, M., and Gonzalez A., (2005). “Preliminary quantitative
assessment of earthquake casualties and damages.” Natural Hazards, 34(3), 353-474.
Bhaduri, B., Bright, E., Coleman, P. and Urban, M. (2007). “LandScan USA: A High
Resolution Geospatial and Temporal Modeling Approach for Population Distribution and
Dynamics.” Geo Journal, 69(1-2), 103-117.
Blanchard-Boehm, R. (1998). “Understanding public response to increased risk from
natural hazards: application of the hazards risk communication framework.” International
Journal of Mass Emergencies and Disasters, 16(3), 247-278.
Bonabeau, E. (2002). “Agent-based modeling: Methods and techniques for simulating
human systems.” Proceedings of the National Academy of Sciences of the United States
of America, 99(2), 7280-7287.
Brooks, N. (2003). “Vulnerability, risk and adaptation: A conceptual framework.”
Working Paper 38, Tyndall Centre for Climate Change Research, University of East
Anglia, Norwich.
Brown, C. A. and Graham, W. J., (1988). “Assessing the Threat to Life From Dam
Failure.” Water Resources Bulletin, 24(6):1303-1309.
Carter, T. M., Kendall, S., and Clark, J. P. (1983). “Household response to warnings.”
International Journal of Mass Emergencies and Disasters, 1(1), 95-104.
Clason, C. (1983). “The family as a life-saver in disaster?” International Journal of Mass
Emergencies and Disasters, 1(1), 43-62.
Cova, T.J. (2005). “Public safety in the urban-wildland interface: should fire-prone
communities have a maximum occupancy?” Natural Hazard Review, 6(3), 99-108.
Cova, T. J., and Johnson, J. P. (2002). “Microsimulation of neighborhood evacuations in
the urban – wildland interface.” Environment and Planning A, 34(12), 2211 – 2229.
Cutter, S. (1996). “Vulnerability to environmental hazards.” Progress In Human
Geography 20(4), 529–589.
Cutter, S. L., Mitchell, J. T., and Scott, M. S. (2000). “Revealing the vulnerability of
people and places: a case study of Georgetown County, South Carolina.” Annals of the
Association of American Geographers, 90(4), 713–737.
DeKay, M. L. and McClelland, G. H., (1993). Predicting Loss of Life in Cases of Dam
Failure and Flash Flood. Risk Analysis, 13(2), 193-205.
Drobot, S.D., Benight, C., and Gruntfest, E., (2007). “Risk factors for driving into flooded roads.”
Environmental Hazards, 7(3), 227-234.
Epstein, J. M., Cummings, A. T., Chakravarty, S., Singa, R. M. and Burke, D. S. (2002)
“Toward a containment strategy for smallpox bioterror: an individual-based
computational approach”, Center on Social and Economic Dynamics, Working Paper No.
French, J., Ing, R., von Allmen, S., and Wood, R. (1983). “Mortality from flash floods: a
review of the national weather service reports, 1969 – 1981.” Public Health Reports, 98
(6), 584 – 588.
French Ministry of Sustainable Development (2009) “Burst of a dam: 2 December 1959
Malpasset [Var].” French Ministry of Sustainable Development, France.
Gillespie, D. F. and Murty, S. A. (1991). “Setting boundaries for research on
organizational capacity to evacuate.” International Journal of Mass Emergencies and
Disasters, 9(2), 201-218.
Gladwin, C. H., Gladwin, H., and Peacock W. G. (2001). “Modeling hurricane
evacuation decisions with ethnographic method.” International Journal of Mass
Emergencies and Disasters, 19(2), 117-143.
Goltz, J. D. (1984). “Are the news media responsible for the disaster myths?: a content
analysis of emergency response imagery.” International Journal of Mass Emergencies
and Disasters, 2(3), 345-368.
Grimm, V., and Railsback, S. F. (2005). “Individual-based Modeling and Ecology”.
Princeton, NJ, Princeton University Press.
Gupta, A. and Yadav, P. (2004). “SAFE-R: a new model to study the evacuation profile
of a building.” Fire Safety, 39(7), 539 - 56.
Gwynne, S., Galea, E., Owen, M., Lawrence, P.J., and Filippidis, L. (2005). “A
systematic comparison of buildingEXODUS predictions with experimental data from the
Stapelfeldt trials and the Milburn House evacuation.” Applied mathematical modeling,
29(9), 818–851.
Hartford, D. N. D., and Kartha, C. V. (1995). “Dam breach inundation and consequence
evaluation. How safe is your dam? Is it safe enough?” British Columbia Hydro
Cooperation, Canada, Report no. MEP11-5. 264 p.
Hervouet, J-M. (2000a). “TELEMAC modelling system: an overview.” Hydrological
Processes, 14(13), 2209–2210.
Hervouet, J-M. (2000b). “A high resolution 2-D dam-break model using parallelization.”
Hydrological Processes, 14(13), 2211-2230.
Hultåker, Ö. (1983). “Introduction: family and disaster (special issue).” International
Journal of Mass Emergencies and Disasters, 1(1), 7-18.
International Strategy for Disaster Reduction (2004). Living with Risk: A global review
of disaster reduction initiatives. UN Publications, Geneva.
Johnstone, W., Assaf, H., Sakamoto, D., and Hartford, D. (2003). “Analysis of the
Malpasset dam failure using GIS and engineering models” Proceedings of GeoTec 2003.
Johnstone, W.M., Sakamoto, D., Assaf, H., and Bourban, S. (2005). “Architecture,
Modelling framework and validation of BC Hydro’s Virtual Reality Life Safety Model.”
Proceedings of the International Symposium on Stochastic Hydraulics, Nijmegen, the
Kirschenbaum, A. (1992). “Warning and evacuation during a mass disaster: a
multivariate decision making model.” International Journal of Mass Emergencies and
Disasters, 10(1), 91-114.
Kirschenbaum, A. (2006). “Families and disaster behavior: a reassessment of family
preparedness.” International Journal of Mass Emergencies and Disasters, 24(1), 111–
Lind, N., Hartford, D., and Assaf, H. (2004). “Hydrodynamic Models of Human Stability
in a Flood.” JAWRA Journal of the American Water Resources Association, 40(1) 89-96.
Penrose, A. and Takaki, M. (2006). “Children’s rights in emergencies and disasters.” The
Lancet, 367(9511), 698-699.
Macy, M. W. and Willer, R. (2002). "From factors to actors: Computational sociology
and agent-based modeling", Annual Review of Sociology, 28(1), 143-166.
McDonald, D. J, (2006). “Images of America: The Teton Dam Disaster”. San Francisco,
CA, Arcadia Publishing.
Perry, R. W. (1989). “Taxonomy and model building for emergency warning response.”
International Journal of Mass Emergencies and Disasters, 7(3), 305-327.
Perry, R. W. and Lindell, M. K. (1991). “The effects of ethnicity on evacuation decisionmaking.” International Journal of Mass Emergencies and Disasters, 9(1), 47-68.
RESCDAM (2000). “The use of physical models in dam-break flood analysis.” Helsinki
University of Technology, Helsinki, Finland.
Reynolds, C. W. (1987). “Flocks, herds, and schools: a distributed behavioral model”.
Comput. Graphics 21(4), 25–34.
Rohde, M.S. (2002). Command decisions during catastrophic urban-interface wildfire: A case
study of the 1993 Orange County Laguna Fire. M.A. thesis, California State University, Long
Beach, CA.
Santos, G. and Aguirre, B. E. (2004). “A critical review of emergency evacuation
simulation models.” Disaster Research Center, University of Delaware, Newark, DE,
Scawthorn, C., Schneider P. J., and Schauer, B. A. (2006). “Natural hazards – the
multihazard approach.” Natural Hazards Review, 7(2), 39.
Schneider P. and Schauer B. (2006). “HAZUS- its development and its future”, Natural
Hazard Review, 7(2), 40-44.
Sorensen, J. H. (1991). “When shall we leave?: factors affecting the timing of evacuation
departures.” International Journal of Mass Emergencies and Disasters, 9(2), 153-165.
Snell, D. and Smith, G. (1959). “Valley is washed in horror when a dam gives way”, Life
Magazine, December 14.
Statistics Canada (1996). GeoSuite, 1996 Census, Catalogue 92F0085XCB.
Turner II., B.L., Kasperson, R. E., Matson, P., McCarthy, J. J., Corell, R. W., Lindsey,
C., Eckley, N., Kasperson, J. X., Luers, A., Mertello, M. L., Polsky, C., Pulsipher, A.,
and Schiller, A. (2003). “Framework for vulnerability analysis in sustainability science.”
Proceedings of the National Academy of Sciences of the United States of America,
100(14), 8074–8079.
Verburg, P. H., Schot, P. P., Dijst, M.J., and Veldkamp, A. (2004). “Land Use Change
Modelling: Current Practice and Research Priorities,” GeoJournal , 61(4), 309-324.
Walsh, M., and Benning, N. (1998) “Defining Flood Hazard in Urban Environments.”,
Proceedings of the Second SIA Regional Conference on Stormwater, Stormwater Industry
Association, Batemans Bay, Australia, April 27-28.
White, G. F., Kates, R.W., and Burton, I. (2001) “Knowing better and losing even more:
the use ofknowledge in hazards management.” Environmental Hazards, 3(3-4), 81–92.
Zhai, G., Fukuzono T., and Ikeda S. (2006). “An Empirical Model of Fatalities and
Injuries Due to Floods in Japan.” JAWRA Journal of the American Water Resources
Association, 42(4), 863–875.
Table 1. Overview of models used in disaster risk assessment and emergency planning
US Bureau of
Input data: total population
at risk (PAR), warning time
(USBR) method
of dam
and average water depth;
(Brown and
breach and
Graham 1988)
flash floods
Methodology: a nonlinear
equation developed based on
22 cases of dam breach
failures and flash floods;
Extreme events were
Pros: simple to use;
Cons: cannot be used to
estimate loss of life based on
sub divisions of PAR; does
not represent extreme events.
Dekay and
Input data: total PAR,
McCelland (1993)
warning time, and flood
of dam
lethality (assigned 1 if 20%
breach and
or more of the buildings are
flash floods
destroyed, zero otherwise);
Methodology: based on a
large set of flood events.
Pros: considered the most
rigorous of all empirical
formula as it was developed
on a much larger database;
Cons: cannot be used to
estimate loss of life at more
than two subdivisions of
Zahi et al. (2006)
Input data: number of flood
affected residential buildings
of flash
and whether the event is a
floods and
flash flood or rainy season
low level
Methodology: developed
based on a large set of
Japanese flood events.
Pros: Easy to use.
Cons: can produce great
variability in estimates since
it relies on logarithmic
transformation and does not
differentiate between severely
or lightly impacted buildings
(Assaf 2007).
Badal et al. (2005)
Input data: population
density, earthquake’s
magnitude and duration;
Methodology: the formula
was derived based on logtransformed regression
analysis of earthquakes
events in the 20th century.
Pros: easy to use in risk
assessment studies;
Cons: logarithmic
transformation can results in
great underestimation of large
(Gupta and Yadav
Input data: representation of
management evacuation
evacuation environment
of buildings
(stairs, rooms, hallways, etc.)
as a network of nodes, the
number of people in each
node, travel time between
nodes, and designation of
destination nodes;
Methodology: the model
simulates evacuation of
people from buildings;
network optimization
techniques are used to
determine optimal evacuation
Pros: captures physical
characteristics of evacuation
Cons: No representation of
social interaction.
Input Data: evacuation
management evacuation
space is represented as a grid,
of buildings
people and obstacles are
and Aguirre 2004)
represented as grid cells,
hazard (chemical spill, etc.)
can be also represented as
grid points;
Methodology: based on the
cellular Automata approach;
Pros: individuals can be
assigned a limited number of
characteristics (e.g. speed);
computationally less
Cons: does not represent
social interaction.
BuildingEXODUS Emergency
(Gwynne et al.
Input data: selection of
management evacuation
nodes and arcs representing
of buildings
spaces and distances between
them, respectively; range of
values representing
characteristics of population
(e.g., age, weight and agility);
Methodology: simulates
evacuation of people from a
building; the evacuation
environment is represented as
an interconnected set of
nodes and arcs; evacuee are
represented individually with
a set of characteristics and
rules determining their agility
and behavior;
Pros: provides more realistic
representation of the
evacuation environment;
ability to simulate toxicity;
Cons: data intensive.
(Cova and
Johnson 2002)
Input data: road network
management evacuation
and parcel data, destinations,
of natural
and statistical information
hazards (e.g.
used in estimating number of
departing vehicles and
departure timing;
Methodology: Cova and
Johnson (2002) used a
commercial traffic simulation
software, Paramics, to
simulate evacuation scenarios
based on trips created by a
custom scenario generator;
Paramics simulates individual
vehicles movements in a
transportation network;
Pros: Leveraging of a well
established commercial
Cons: does not facilitate
multi-scenario analysis; no
clear guidance on selection of
statistical parameters.
Input data: Census data,
(Bhaduri et al.
land use, transportation,
imagery, elevation,
educational institutions and
Methodology: provides
estimates of nighttime and
daytime population.
Nighttime population is
estimated by distributing the
population for each census
block over 1 arc-second grid
based on weights
representing land use,
proximity to places of
activities (schools, parks
etc…) and transportation
elements. The daytime
population is estimated based
on information related to
work and school activities.
Pros: provides a global
coverage and comprehensive
coverage for the USA at 3
arc-second (about 90 meters)
Cons: Temporal distribution
only covers nighttime and
baseline daytime populations.
BC Hydro Loss of
Dam risk
Input data: census data,
Life (LOL) model
cadastral and building use
data, elevation, hydraulic
(Assaf et al. 1997)
modeling, and dam operation
Methodology: Census data is
distributed based on building
use data. Daily, weekly and
yearly occupancy factors
determine the temporal
distribution of population.
The model simulates the
escape of population away
from flood path. It estimates
survivability based on water
depth and velocity and
individual characteristics.
Pros: Provided a novel
approach to estimate spatial
and temporal distribution of
population under the risk of a
dam failure.
Cons: A one-dimensional
model with no explicit
representation of individual
BC Hydro Life
Dam risk
Input data: census data,
Safety Model
cadastral and building use
data, topography, information
(LSM) (Assaf and
Hartford 2002)
on workers student and
hospitable, GIS in addition to
field surveys.
Methodology: utilizes agents
technology to model peoples’
behaviors and interactions;
simulates vehicular and
pedestrian movement; and
simulate fate of each
individual as function of
physical robustness and
exposure to flood water.
Pros: a significant
enhancement of the BC LOL
by simulating explicit
individual interaction and
movement in a more realistic
two dimensional virtual
Cons: strictly applicable to
flood disasters.
The Hazards U.S.
Input data: GIS based
hazard loss
database on buildings and
census data, buildings
(Schneider and
damage functions for three
Schauer 2006)
main hazards: earthquakes,
hurricanes and floods.
Methodology: Provides
impact assessment of main
hazards at three levels of
ascending complexity and
accuracy. At the lowest level,
the model provides
preliminary assessment
utilizing information from a
default database. At higher
levels analysts can utilize
more detailed information to
develop more reliable
Pros: provides a USA nationwide tool for hazard and risk
Cons: calculate loss of life
only for earthquake hazards.
Does not simulate evacuation.
Table 2 Example of a whereabouts table for a full-time worker.
Time of Year
September 1 to April 30
May 1 to August 31
Time of Week
Weekdays (Mon-Fri) Weekends & Statutory Weekdays (Mon-Fri)
Time of Day
8am- 5pm- 10pm- 10am-8pm 8pm-10am 8am5pm 10pm 8am
Weekends & Statutory
Frequency of
0.1694 0.0941 0.1883
period per Year
At Home
At Work
At School
0.0871 0.0484 0.0968
On Road
Out of Area
Fig. 1. Architecture of the Disaster Modeling Framework
Time of
Level (m)
Time of
8am - 5pm
JAN1 - APR31
5pm - 10pm
10pm - 8am
10am - 8pm
8pm - 10am
8am - 5pm
MAY1 - JUN30
5pm - 10pm
10pm - 8am
10am - 8pm
8pm - 10am
8am - 5pm
JUL1 - AUG31
5pm - 10pm
10pm - 8am
10am - 8pm
8pm - 10am
8am - 5pm
SEP1 - DEC31
PAR = Population at Risk
LOL = Loss of Life
5pm - 10pm
10pm - 8am
10am - 8pm
8pm - 10am
Fig. 2. Output from a dam safety DMF application
Fig. 3. Streamlining CSM output in the DMF
Fig. 4. The Topology of the CSM Objects
Enumeration Areas (EAs)
Places of Residence-(PoR)
Buildings (Bldgs)
Residential Groups (RGs)
Fig. 5. Example showing the process of populating the static view’s GIS database
P/G : Person or Group
Bldg: Building
Vhcl: Vehicle
P/G Receive
Stay in Bldg
Decide to
Use Vhcl
P/G Stay in
P/G Walk
Contact w
Contact w
P/G in Vhcl
Contact w
Injured or
P/G Injured
or Killed
Fig. 6. The ESM central algorithm
Fig. 7. Snapshots of a dam breach/community simulation scenario