A SOA-based Decision Support Geographic Information System for Storm Disaster Assessment

Zongsheng Zheng*, Dongmei Huang, Jianxin Zhang, Shengqi He

College of Information Technology Shanghai Ocean University

Shanghai, China *Corresponding author: zszheng@shou.edu.cn

Zhiguo Liu East Sea Information Center State Oceanic Administration

Shanghai, China

Abstract—SOA (Service-Oriented Architecture) is an architecture of software design suitable for changing environment, in which the difficulty in the sharing of data and operations. In this paper a SOA-based decision support system (DSS) was built for assessing the short-term risk of storm disaster at coastal area of Shanghai. This was done by integrating a hydrodynamic model and a disaster assessment model into geographical information system (ArcGIS Server 9.3) as a web service using ArcObjects in ArcGIS and Java. Storm flood was simulated by hydrodynamic model at the Yangtze estuary. Population, house area, important units and submerge water depth were considered as impact factors to build storm-tide evaluation index system, with which the influence of storm flood was acquired by quantitative evaluation model using fuzzy method. Geographic information system was investigated to show storm flood process and disaster assessment results in two and three dimension map. According to the assessment results, Optimization Algorithm was employed to calculate afflicted population assigned to available refugee settlements. And optimal retreat routes were provided by Shortest Path Algorithm according to the traffic information of Shanghai. To meet the business demands, the two algorithms were published as web services and integrated into DSS through web services composition(WSC). With the support for 3D geospatial visualization capabilities of Skyline software, this system can provide convenient interfaces for rendering the output in 3D display. The results show that SOA is an effective way to solve information isolated island, integrate heterogeneous computing systems and realize data and services sharing. The system demonstrates the feasibility of implementing information systems that are interoperable, low-cost, web-based, and which have a high evolution capacity through web services. This DSS can help disaster prevention department to analyze and visualize (charts, maps) the possible effects of storm events on both the immediate and long-term risks of flood damage at different regional level, which supplied convenient approach for making scientific policy decision of the flood control and disaster alleviation.

Keywords- shanghai; GIS; SOA; Storm Disaster; disaster assessment model; Assistant Decision

I. INTRODUCTION Storm surge is a weather event determined by low pressure,

high wind speeds and high waves, all associated with a typhoon as it makes its landfall. The fast sea level raise in coastal areas can cause severe flooding and cost lives,

particularly when the storm surge coincides with high tides. Wind waves superimposed on such a combined storm tide can also cause significant damage to the coastal infrastructure. With a warming climate, there are concerns that such events may increase in frequency and intensity due to a combination of rising sea level and an increase in the frequency of extreme weather[1]. Storm surge can cause serious coastal hazards at most shore of the world, which ranks first in the marine disasters. China has a long coastline extending over 18,000 km, which is always suffered by typhoon in the summer and autumn. While in the spring and winter, it is often subject to strong winds induced by cold air, which is prone to storm surge disasters. According to statistics, about 34 percent of tropical cyclones including typhoons and tropical storms in the northwest pacific coast makes landfall in China. With a total of more than 183 km of coastline and about 1900 million people, Shanghai is by far the most important coastal city of China. It is often affected by typhoons on an average of twice a years, even 6-7 times. If the typhoon occurs during spring tide, storm surges would result in severe storm surge disasters.

In order to reduce disaster losses, there is an urgent to construct the computer system in combination of storm disaster analysis and prediction, risk assessment and web publishing. The system can provide decision makers with macrocosm and microcosm information, the real-time and historical information, socioeconomic and engineer information and efficiently handle the large and complex information involved.

China has succeeded in monitoring and evaluating on severe floods using GIS and RS technology since 1991. In 2000, Jiangsu province established a real-time hydrological information display and analysis system[2]. Shanghai has also built a Shanghai flood control and auxiliary decision-making system based on ArcGIS[3]. A GIS-based Haihe water resources information system has been set up by Chinese Academy of Surveying and Mapping (CASM) using Geo-Windows software[4].

However, the difficulty of marine information systems establishment lies in achieving the exchange and sharing of marine data from different acquisition devices, processing platforms and storage formats, which are always collected by various departments and even different sectors in the same department with individual purposes. The heterogeneous data and systems lead to the common existence of “information

Ocean Public Welfare Project of State Oceanic Administration, China(200805016); Scientific Research Starting Foundation for Doctors, Shanghai Ocean University (070304) and Science Foundation for The Excellent YouthTeachers of The Shanghai Education Commission (ssc08018).

isolated islands” [5]. To solve the “information isolated islands”, this paper investigated web services to implement a storm surge disaster assessment and decision-making system under the framework of Services-oriented Architecture(SOA). The system can easily integrate marine data and services from different websites, provide accurate, timely and comprehensive information support for analyzing flood situation, making risk assessment and developing strategy for disaster control and reduction.

II. SOA FRAMEWORK AND GIS TECHNIQUE

A. Service-oriented Architecture Service-oriented architecture (SOA) is a novel concept for

constructing information system architecture. It provides a new solution to integrate among heterogeneous systems by opening semantics of message passing. A SOA combines the ability to invoke remote objects and functions (called ‘‘services’’) with tools for dynamic service discovery, placing an emphasis on interoperability. SOA can be realized by Web Services technologies [UDDI (Universal Description, Discovery and Integration), WSDL (Web Services Description Language), SOAP (Simple Object Access Protocol), etc.] as a set of flexible and interoperable standards for distributed systems[6].

Web service is the preferred standards-based way to realize SOA, which is a technology based on the Internet that is defined by the W3C as ‘‘A software systems designed to support interoperable Machine for Machine interaction over a network”. Web services can be used to implement a framework based on the SOA concepts which is the basic element of communication for a message instead of the operation.

The main difference between Web Services and traditional approaches, such as the distributed objects technologies from the Object Management Group (OMG), Common Object Request Broker Architecture (CORBA) or the Distributed Component Object Model (DCOM) from Microsoft, lies in the aspect of loose coupling of the architecture. Instead of building applications that result in collections of objects or components that are firmly integrated, that are well-known and understood in development time, the service approach is much more dynamic and is able to find, retrieve, and invoke a distributed service dynamically. Figure 1 shows the service-oriented architecture.

B. Geographic Information System Geography Information Systems have been improving

since 1970s. GIS is the integrate systems which can obtain, store, manage, analyze and demonstrate natural phenomena to combine the other information. In recent years, shifting the focus field of urban planning, land use, mapping, environmental protection, electricity, telecommunications, disaster prevention etc., GIS technology have received increased attention in global environmental change and marine resources and environmental management. "Digital Ocean" is an important part of "Digital Earth". On basis of the construction of the marine database, the introduction of GIS to the marine field can realize the automated cartography and

sharing of marine information which is in line with development trends of "Digital Ocean".

Geographic information sharing has experienced two stages: file-sharing and spatial database sharing. Now with the development and application of services, Service GIS, a new model characterized by sharing services ushered in a service-oriented geographic information sharing. Based on GIS components, Service GIS encapsulates all the GIS functionalities as web services by applying service-oriented software engineering methods, which realizes the cross-platform, cross-network and cross-language interaction[7-8]. Service GIS also has the ability to aggregate the services from other GIS servers. With the adherence to the standards of SOA and spatial information services, Service GIS facilitates rapidly building business applications for the developers by seamlessly integrating heterogeneous IT business systems.

Figure 1. Service-oriented architecture.

III. SYSTEM ARCHITECTURE This section is based on SOA which is described in Section

II and the standard which is applied to the SOA is to establish urban storm disaster assessment and decision support system integrated platform. The system strictly follows the design pattern of Model-View-Control (MVC) to ensure rapid development and higher maintainability[9]. Each module was developed according to the data layer, component layer, service layer, logic layer and display layer, which achieves the separation of business logics from the foreground and background. In Figure 2, it is shown how to use the services and to design the platform structure of this system.

Figure 2. Architecture of the system

View

Model

Controller

SystemSecurity

Spatial Database Attribute Database Model Library

Map ServicesGeoprocessing、Network Analysis

ServicesThematic Map

ServicesStatistical Analysis

Services

Water Model Calculation Service

Risk Assessment Service

Browser

Database Operation ServicesPrediction

Systems

Existing Systems

Data Resource

Information Center

OtherBranchs

PredictionCenter

View Layer

Service Layer

Service Layer

Data Layer

MonitoringCenter

Monitoring Systems

Component Layer

……

Business Logic Layer

A. Data Layer 1) Spatial Database: Spatial databases are such systems

designed specifically to include data with spatial attributes, such as geographical location, distance, and extent. Basic geographic data in this system includes topographic data, bathymetric data, 2.5m SPOT satellite images, 0.6m Quick Bird images, tidal stations, coastal embankments, roads and rivers information. Thematic data mainly refer to various districts information and model computing grid information used in the hazard assessment process. Spatial vector datas were layered in accordance with classification and coding rules (GB104.14). Then the coordinate and projection conversion was made according to the standards of marine information. Finally, vector and raster data were stored directly into Oracle database through ArcGIS Spatial Data Engine(SDE). Any other system can directly invoke web services (GeoData Service), which access spatial database by ODBC or JDBC.

2) Attribute Database:Attribute data is the descriptive data that GIS links to map features. Attribute data is collected and compiled for specific areas like states, census tracts, cities, and so on and often comes packaged with map data. Business attribute database of the system primarily stores the information of important factors of disaster assessment which includes the population, the number of important enterprises and the housing area of affected streets and districts. The dynamic calculation results of storm model are saved as database table, in which the attribute information and spatial information are interrelated by keyword ID.

3) Model Library • Hydrodynamic model: Three-dimensional simulation

for the Yangtze Estuary is performed by using the DHI Mike21 model adopting the shallow water equations and the finite volume method on a curvilinear body-fitted grid. Mike21 is a software package that was designed primarily as an application focused on water flow and quality. The package consists of several modules coupled together to provide a complete picture of three-dimensional flow, surface waves, water quality, ecology, sediment transport and bottom morphology in complicated, coastal areas[10]. After initial parameters are inputted, the model will provide water level and current velocity at each gird.

• Disaster assessment model: ),,2,1( niiA = are n

fuzzy subsets in the field of U ，which are combined into a standard library. For Uu ∈0 ，if

))(,),(),(max()( 0000 21uuuu

ni AAAA μμμμ = (1)

0u belongs to the ith pattern relatively, which is in n fuzzy subsets of ),,2,1( niAi = according to Maximum Subordination Principle. Each grid with water elevation was substituted in membership function of flood grade, the value of

}5,,2,1|{ 1 =jr nj represents the membership degree

to jth flood grade at the nth grid. If }5,,2,1|max{ 11 0

== jrr nj

nj

，the flood grade

satisfies ),,2,1;51( 001 Nnjjd n =≤≤= at the

grid. 0j is the maximum value of the all satisfied.

1.1 Equations Software component layer

B. Software Component Layer In order to reduce software production costs, software reuse

seems to be one of the most promising approaches. Reusing software may greatly increase the productivity of software engineers and improve the quality of developed software. Software component libraries have been suggested as a means for facilitating reuse[11]. Domain analysis was made on existing information systems and model libraries for extracting reusable and independent components. According to the ocean business, spatial operation component, measurement component, selection component, map query component, thematic map component and spatial analysis component were packaged as JavaBean or DLL, which were stored in component library, where finer-grained service components can be combined into the coarser-grained. For instance, selection and map query component can be integrated into spatial query component and published as web service.

C. Services Layer On basis of models and components, coarser-grained web

services can be packaged and published online for other units to invoke at service layer. Other services can be easily added to the system as they become available. They will benefit from the existence of the infrastructure already developed, which will guide the development of new functionalities in order to maintain the important characteristics obtained (interoperability, flexibility, distribution). These new services do not have to be developed by the same institutions or groups, nor even need to be hosted in the same place. They can be developed and hosted anywhere, provided that they are published on a registry and are available for use. For instance, East China Sea Branch of State Oceanic Administration consists of three sections: the Forecast Center focusing on marine model, the Monitoring Center concerned with marine data collection and handling and Information Center required attention to informationize the marine data. So, Forecast Center can invoke data services of Monitoring Center to verify their model, while the forecast results can be available for other departments. Information Center can show the model results in combination of services from Forecast Center and his own mapping services. At the same way, heterogeneous GIS platforms can also be integrated through standard Web services interfaces. And to meet different business demands, services then can be rearranged to achieve different business processes through Web Services composition (WSC) without encoding or only a small amount of codes.

The services layer includes distributed services (Web Services) that implement the processors of the business logic

for map visualization, geospatial analysis, hydrodynamic model running and risk evaluation.

The following are the services of this system:

• Web map services: Send back the raster and vector file of geography graphics and diagram property.

• Spatial analysis services: This service hopes that it can provide buffering, overlaying, interpolating, routing, etc. as functionalities to user.

• Hydrodynamic model calculation service: Developer can use this service to know the flood information induced by the specific storm.

• Risk evaluation services: With the result of hydrodynamic model calculation service, this service can provide disaster assessment grade for risk decision support.

• Statistic analysis services: The plan of this service is to provide statistical information of the risk assessment results as graphics or charts for users.

IV. IMPLEMENTATION

A. Software Environment The system realizes basic topographic map, remote sensing

images and thematic map data sharing under the platform of ArcGIS Server 9.3. With the help of the components of ArcObjects, map services and GIS functions services were published and managed. The storm dynamic process and evaluation results were visualized by the Skyline software. The depended software and hardware are as follows:

• Database server: Solars operating system with the database management software (Oracle 10g, ArcSDE 9.3) was executed on the database server.

• GIS server: Simple GIS functionalities were used which are related to mapping, tracking, routing, searching, etc, which were published through ArcGIS Server9.3, Three dimension map services were implemented using TerraGate module of Skyline software.

• Development and deployment environment: The system was implemented, using Java. The ESRI ArcGIS Flex Application Programming Interfaces for Rich Internet Applications and ESRI ArcObjects for advanced GIS functionalities were used for the development of GIS module. Windows 2000 or Windows XP was required at client.

B. Module Design Most modules were constructed through loose-coupling

business alignment based on SOA. According to the business rules, The prototype of the decision support system includes service management, flood display, disaster assessment, statistical analysis and extension modules, which has demonstrated the feasibility of implementing information

systems that are interoperable, low-cost, web-based, and which have a high evolution capacity.

1) Services Management Module: In order to discover and invoke the services from other sectors, different sectors are required to register their services in the service management module, where all the services are under unified management of Service Directory. Through the Service Directory which was registered in this system, more users can use this service not only in one system, but also in another system. Administrators can start, stop, delete and identify specific services in this module. Figure 3 shows the platform monitoring servers status of all departments. The window of Service Directory which lists all the registered services is presented in Figure 4.

Figure 3. Servers status monitoring of departments

Figure 4. GIS services lists

2) Storm Flood Process Module: This module can be conveniently combined through services alignment in term of business logic. The distributed web services, web map services from the East China Sea Information Center and hydrodynamic model calculation service from Marine Prediction Center, were employed to visualize the storm flood dynamic process at browser. And the three dimension flood evolution was also easy to implement through 3D data services published by TerraGate, which was shown in Figure 5. In order to promote the display performance, releasing the grid numbers by interpolation was made at web application.

Figure 5. 3D flood dynamic process.

3) Disaster Assessment Module: The four factors including water accretion, population, units and house area of the disaster area were considered in disaster evaluation model which was employed to rank the risk of specific storm surge. Membership function of fuzzy set was investigated to measure the four variables in accordance with Maximum Subordination Principle, which was discussed in Section III. The values of membership for various parameters are presented in TABLE I. And the importance of factors can be specified by users according to realistic situation or Analytic Hierarchy Process(AHP). Then the disaster assessment model considering the above factors can be published as web services by assembling web map services and risk evaluation services. The developer can easily build the web application for the presentation of the assessment results using the services called by ArcGIS Flex API at client. The web browser of the application is shown in Figure 6.

Figure 6. Disaster evaluation result

4) Decision Support Module: GIS provide a wide variety of analysis functions over georeferenced data, and offer advanced cartographic visual presentation, which is used to help decision makers in identifying geographic regions that satisfy one or more criteria, exploring spatial and temporal relations among georeferenced data. This module employed GIS to calculate the shortest path from flood area to safety settlements and draw the retreat routes shown in Figure 7. The user can select the any extent of disaster-afflicted areas, in which the statistics of the flood victims population was automatically made. According to GIS overly analysis, the system can provide the settlements which was not affected for user’s selection. Then victims

alignment to various safeties was obtained by optimization algorithm, which was labeled on the route.

Figure 7. Optimization of evacuation for flood victims

5) Statistical Analysis Module: In order to collect different kinds of information from the specific flood, this system provided statistical results in graphics or charts by calling statistic analysis services, which facilitates analyzing the useful information for decision maker. Water level accretion chart represents the variation of water level with time at specific location shown in Figure 8. In Figure 9, it shows the flood duration time, which is long (red) and short (blue).

Figure 8. Water level accretion chart

Figure 9. Flood duration graphic

V. CONCLUSION 1) Urban storm disaster assessment and decision-making

system visualizes flood dynamic process and disaster evaluation result in 2D and 3D display using GIS technique. According to the exact and timely information of storm

situation, the system can supply convenient approach for making scientific policy decision of the flood control and disaster alleviation.

2) The architecture of storm disaster system based on SOA is an effective way to solve information isolated island by integrating heterogeneous computing systems and realizing data and services sharing in marine digitalization.SOA can

break down information barriers between different business units.

3) Intelligent decision is the inevitable tendency of the development of informatization. In order to make more effective strategies on disaster control and reduction, next research should be focused on promoting intelligence of urban storm disaster decision support system on the basis of disaster evaluation results.

TABLE I. THE VALUE OF MEMBERSHIP FOR VARIOUS PARAMETERS

Grade Description Water accretion(cm) Population (thousand) Units Affected house area(km) Color

0 Very light 0-20 0-30 0-200 0-0.5 Blue 1 Light 20-60 30-60 200-600 0.5-1.0 Yellow 2 Medium 60-110 60-90 600-1000 1.0-1.6 Orange 3 Hard 110-170 90-120 1000-1400 1.6-2.0 Red 4 Very hard >170 >120 >1400 >2.0 Black

ACKNOWLEDGMENT The author wishes to thank Prof. Cheng Su at East Sea

Information Center, State Oceanic Administration for constructive suggestions.

REFERENCES

[1] S.Y.Wang, R. McGrath, J.Hanafin P. Lynch, T.Semmler and P.Nolan, “The impact of climate change on storm surges over Irish waters,” Ocean Modelling. vol.25, pp.83-94,2008.

[2] X.L.Chen,Y.H.Wen,J.G.Dong and L.Y.Huang,“The Application of GIS in Graphic Displaying and Analyzing System of Jiangsu Real-time Water Regime,” Hydrology. vol. 22, pp. 43-46,2002.

[3] X.Y.Zheng,C.L.Hu,“Study on Flood Prevention Aided Decision-making System in Shanghai,” Hydrology. vol. 23, pp. 33-36,2003.

[4] L. J. Zhang, X.M.Cui, H.Y.Peng and Y. H. Wang, “A Study on Nanjing City s Flood Prevention and Disaster Reduction Information System,” Hydrology.vol.5,pp. 6-13,1998.

[5] C. W. Guo, C.J.Shang and N.H.Guan, “A Marine Data Integration System Based on SOA,” Application Research of Computers. vol.2, pp. 151-154,2006.

[6] Y. M. Fang, L.Y.Lin, C.H.Huang and T. Y. Chou, “An integrated information system for real estate agency-based on service-oriented architecture,” Expert Systems with Applications. vol.36,pp.11039-11044,2009.

[7] C. J. Yang, Y.X.Wang, X.L.Wang, P.Dong and D. L. Liu, “Review of the main technologies of WebGIS,” Jounral of Image and Graphics. vol. 6,pp. 886-894,2001.

[8] W. J. Jia, J.Y.Gong and B.Li, “Optimization method of Web Feature Service,” Acta Geodaetica Et Cartographic Sinica. vol.34,pp.168-174,2005.

[9] L. J. He, L.Yi and L.C.Liu, “Loosely coupled and reusable MVC model Web paging design,” Computer Engineering and Applications. vol. 43,pp. 95-97,2007.

[10] W.Q.Lin, S.Q.Lu and J.Z.Jiao, “Water quality and ecological modelling for Yangtze estuary and its adjacent sea,” Chinese Journal of Hydrodynamics.vol.23(5),pp.522-531,2008.

[11] C. Zhang, X.C.Wu,J.Luo and M.S.Hu, “Visual software development based on software structural component library/workflow,” Computer Engineering and Applications. vol. 44(10),pp. 82-87,2008.