201O Second International Conference on Communication Systems, Networks and Applications

Secondary Disaster Prewaming Based on GIS and Subsequent Risk Analysis

CHEN Tao, LI Miao, YUAN Hongyong Center for Public Safety Research, Department of Engineering Physics

Tsinghua University Beijing, P.R.China

chentao.b@tsinghua.edu.cn

Abstract-This paper presents a method to analysis a disaster's subsequent risk in a comprehensive manner by using both GIS and disaster theory. Secondary disaster should always be considered because it can often cause worse problems if people are left unconscious and unprepared. Here GIS was used as an essential tool to seek risk source of secondary disasters which can be induced by original disaster at certain circumstance. The system architecture of the disaster management system was introduced and the relationship between original disaster and secondary disaster was modeled. Sets of necessary conditions were defined and described with tables in database. GIS butTer analysis was used to find the match of the conditions in space and was performed within the impact area given by computational disaster models. Thus, disaster impact area demarcation and secondary disaster risk were considered in a correlative manner for better disaster management.

Keywords- disaster; risk; GIS; secondary

I. INTRODUCTION

People have always been fighting with all kinds of disasters. With the fast development of human society, lifeline systems, transportation systems, various industrial facilities and concentration of population in cities have tremendously increased not only vulnerability but also risk of our world. Also, under the background of global warming, people now have to face and bear much bigger disaster risk than ever.

Disaster management is a very complex topic to both scientists and government officers. It is complex not only because disaster itself is hard to understand and predict, but also because disaster management is information intensive. Managing disaster, people need to know the evolution, impact and trend of a disaster, nevertheless, make quick and right decisions. Large volumes of information have to be gathered, processed, analyzed, and shared with a broad range of users.

Modern information technology has been used to support disaster management. Computer programs and systems make it easier for managers to communicate with one another, access data, and analyze information. Management information systems such as decision support systems (DSS),

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have been developed and studied since the end of 1960s [1]. In disaster management area, Moore and Abraham [2] have argued that an emergency management system can be viewed as a large-scale man-machine decision-making system concerned with optimal resource allocation, scheduling, and planning. Intelligent decision support systems based on computational models for emergency response are studied in various literatures [3-8].

Post researches focused on the modeling and risk analysis of a single disaster. However, historical cases and recent practices in disaster management have posed the question how secondary disaster can be investigated and not be ignored. Indian Ocean Tsunami in 2004, China Songhuajiang River Pollution in 2005, China Sichuan Earthquake and China Ice Storm in 2008, all these disasters have demonstrated the importance of understanding secondary disaster risk. For example, Songhuajiang River Pollution was caused by petro-chemical explosion and further caused drinking water crisis in downstream cities and diplomatic crisis with Russia. These disasters indicated that a disaster is usually not an isolated occurrence, but often has interaction with others, inducing a number of secondary disasters and bringing much more risk to our society. How to find out the subsequent risk of secondary disasters needs synthetic investigation. In this paper, this problem was partly solved by searching for the risk sources and investigating the possibility in the impact area with the help of GIS technology in spatial analysis.

II. PRINCIPLE

A. GIS and Disaster Models

When we talk about disaster, spatial information expressions are always need, the hot spot, the impact area, the location of refuges, the location of rescue teams, etc. Floods, earthquake and typhoon as much as toxic spills, or explosions are all spatially distributed problems. The risk of disaster has an obvious spatial dimension.

GIS is a tool to store, capture, manipulate, process, and display spatial or geo-referenced data. Topography, census, roads, utilities, bathymetry, elevation, geology, land cover, hydrology and administrative boundaries are examples of

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data types utilized. In GIS, the basic concept is one of location, of spatial distribution and relationships; the basic elements are spatial objects. GIS and its capability to map risks is clearly a powerful tool for risk assessment [9, 10]. The analytical capabilities of GIS are usually limited to static analysis of buffers and overlay layers. To fully utilize the potential of GIS, it should be dynamically used together with computational models to explore whether or not there is potential risk.

Spatial dimensions in risk assessment cover closely related aspects: the risk sources are located or distributed in space, such as a chemical process plant, a dam, landslide area or a transportation system; the original phenomenon of an disaster is spatially distributed like the blast from an explosion or a toxic plume, and the impacts are spatially distributed due to the interaction of the original phenomenon and the receiving system affected.

GIS helps to distinguish and visualize: a). spatial effects in the propagation starting with the location of the hazard source such as epicenter and around seismic zones of different levels; and b). spatially distributed impacts resulting from the earthquake to various vulnerability, for example, chemical storage destroyed by the shock. Most of the classical disaster problems of risk assessment and management are related to these two basic spatial effects. The receiving system or object under the impact of an original disaster is the origin of next strike, which means it may be a risk source to generate secondary disaster. If we can identifY those risk sources, GIS can help to find them in the investigating space.

Almost each disaster and each process in emergency may be modeled in physics and mathematics. Most computational models used in decision support for disaster evolution analysis, impact analysis are GIS referenced. These models are developed to explain and image the disaster mechanism and its impact. In disaster dynamic modeling, the process is expressed in terms of numbers, mass, or energy of interaction and dynamics. The basic elements are species, which may be chemical, environmental media, such as air, water or sediment, human beings, force or energy and their evolution over time. Their distribution may be visualized using coordinates of points, lines or areas and their evolution over time may be visualized using versicolor overlay layers of nephogram on GIS. Effective use of GIS as a geographic data access and spatial analysis platform and visualization tool for the simulation of disasters are needed very much. FEMA's Hazard-MH and many other currently used disaster management information systems have already integrated disaster models with GIS.

B. Modeling Secondary Disasters

A disaster chain maybe established based on investigations and analysis of a large number of history incidents. It describes the derivatives and secondary relationship between disasters. The start point of the chain is defined as an original disaster, which may induce several derivatives and secondary disasters that might also be coupling. Those disasters construct a meshwork including several subchains. Effective disaster management means that

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we should prevent and control the secondary disasters as well as the original disaster.

The sketch of disaster chain is shown in Fig. 1. It consists of nodes and links. The nodes represent the possible secondary incidents from the original disaster. For example, an earthquake may cause landslides, dam burst, tsunami, building collapse, fires and lifeline destruction. The links represent the evolvement between disasters.

-e

o node _link

Secondary disasters

n'h

Figure 1. Expression of disaster chain

The interaction relationship between two disasters can be summarized to three kinds of hazard factors as: energy(heat, force, power etc.) transfer, mass(gas, liquid, solid etc.) transfer and information(temporal and spatial information) transfer, these interactions are trigger and evolution conditions [11]. Current study on disaster chain shows that different strategy should be undertaken at different phases. The use of disaster chain will give positive instructions in the progress of disaster response. Firstly, in the early phase of gestation, which will last for about 70 percent of time in the link, truncation of the link should be the primary method to stop the evolvement. In this phase, the link could be prevented or controlled. Secondly, in the latency phase of great possibility, which will last for about 25 percent of time in the link, undertaking defensive measure should be the proper response. In this phase, the link can hardly be controlled. Thirdly, in the phase of eruption, which will last for about 5 percent of time in the link, only passive treatment could be conducted. In this phase, the link is formed and brings destruction.

III. IMPLEMENTATION

A. System Architecture

Computer models that simulate natural or manmade events require a variety of data sets. These databases include geographic database and topic oriented databases. The former storing terrain, city buildings and street maps. The later includes data about hazards, populations, hospitals, rescue teams, city refuges etc. All these data were referenced with coordinates. They can be obtained from several government agencies. As an example, flood-modeling programs require information on the type of soil, land use characteristics, and elevation points in the study area. GIS provides precise definition of ground elevations, surface features (type of land cover) and weather conditions. In many cases, the data is accurate and meets engineering

quality standards. Direct access to data sources is utilized in many complex disaster models. In the system, the models share common software platform and databases. GIS service manipulates all the geographic related data. ESRI ArcGIS was used as the GIS platform, which provides excellent support for spatial data operation and analysis. The data access services, model components configuration and running control, simulation of models, assessment of subsequent risk such as secondary disaster and life lost, as well as visualization system were all integrated in this architecture. During a disaster management process, a series of real time information still needs to be collected and provided to the models, such as weather condition, sensor data, scene conditions and situation reports. Moreover, this system may get some simulation results directly from other system. Those data was plugged in by predefined XML (extensible markup language) files. The system architecture is shown in Fig. 2.

Overall risk assessment

Secondary disaster

prediction Impact and lost

assessment

Disaster model ing and computational simulation

I GIS service I I Data access II Model config.

Database & geodatabase Knowledgebase

Model components

Plug-in data

files

rXML I Figure 2. System architecture of disaster management system

Other information technologies, such as hypermedia and virtual reality, network computing, lightweight mobile clients, GPS and GSM, can be integrated into this information management and decision support systems for better risk management. This can be done through visualization system and user interface system.

According to the disaster chains stored in database, we can quickly determine its secondary disasters when a disaster occurs. In order to standardize the disaster querying in database, all the disasters listed in our national response plans were classified and coded using uniform coding rule. Each disaster was enumerated and given a unique code respectively in this system for both information reporting and chain storing.

B. Build Relationship between Disasters

Fig. 3 detailed the relationship between two disasters. Disaster was deconstructed into three phases: firstly, born

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from a risk source; secondly, evolved in the environment; thirdly, release hazard factors.

Original disaster

o ,--_---'A ...... __ �

Model simulation

Secondary disaster

o ,--_---'A ...... __ �

Matching GIS analysis tables in space

Figure 3. Analytical view of relationship between disasters

• Build matching tables. A disaster must go through the necessary step to induce a secondary disaster: hazard factors of original disaster affect on risk source of the secondary disaster. Thus, we may build a set of necessary conditions (in other words, rule base) between these two disasters. Three kinds of relationship should be described, which are "disaster to hazard factors", "factor to risk source" and "risk source to disasters". One table was used to describe what hazard factors a disaster may contain. One table was used to describe what risk source a hazard factor may affect. And the other one was used to describe what kind of disaster a risk source may cause.

• Code disasters. To build the information system, lots of disasters should be identified and easily expressed with computer language. Disasters under investigation were all classified into four categories: nature disaster, manmade disaster, public health events and social security events. Disaster code was used instead of disaster name for robust computer programming.

C. Model Simulation and GIS Analysis

Disaster simulation will give out the intensity and spatio­temporal distribution of the hazard factors release by a disaster. GIS provides the spatial analysis and graphic representation of the geographic maps, model output data and scenario. It reads the information from the simulation model output data and displays them by various means. The visualization is often realized by overlaying data layers on geographic maps for better understanding and analysis of the data and simulation results.

For secondary disaster risk assessment we must use GIS to manipulate the data layers and hazard zones given by the simulation model.

• Seeking risk source in impact area. GIS based spatial analysis is an important tool to find weather a risk source exists within the investigating area. Geographic buffer analysis includes points, lines, and polygons type operation method. It can easily find those objects of interest without missing any one from databases of Mega or Giga Byte large. If no risk source is found, no secondary disaster will

occur. Otherwise, a disaster chain routing procedure will continue.

• Disaster chain routing. A tree data structure was used to describe the disaster chain shown in Figure 1. Original disaster was defined as the root node of a tree. By traversing the tree while querying the tables build in the database, we can establish routes that an original disaster may go through all the factors to induce a secondary disaster. The probability of secondary disaster can be estimated using chain theory [12].

All we need is the output of simulation model. Fig. 4 shows a typhoon's impact and its secondary disaster risk sources. A model may not run in this system, but we must define standard interfaces to let the result in. Typhoon model is very complex but it can give perfect output data on map. These data can be collected from weather forecasting agencies. The dams, lakes, landslide areas were all matched out from the database. Then the secondary disaster prewarning includes dam breaking, flood and landslide.

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Figure 4. Example of GIS analysis for risk source

IV. CONCLUSION

The subsequent risk analysis of secondary disaster is a novel way to understand and cope with disasters. Disaster relationship and ways to investigate secondary disaster for prewarning was discussed in this paper. This recent development provides a systematic view in disaster management. GIS is now developing into a common technology. Integration of GIS and computational models was able to give hazard zones, while GIS analysis and analytical method based on disaster chain theory may help to

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find out whether there are risk sources to cause secondary disasters. It may provide great benefits in spatial analysis and disaster management, prevention, prewaming and decision making.

Some computational models are too complex to be integrated with GIS or they may not provide spatial distribution of hazard factors. For example, some social security events, they may need further study and may be solved in other ways to find their potential secondary risk. Research by the academic agencies will result in continuing developments in disaster chain theory and practical disaster models. Powerful computers and flexible GIS software advances may in the future support dynamic and 3D analysis models.

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