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International Journal of Computer Science Engineering and Information Technology Research (IJCSEITR) ISSN 2249-6831 Vol. 3, Issue 2, Jun 2013, 341-350 Š TJPRC Pvt. Ltd.

A SURVEY ON GIS BASED DECISION SUPPORT SYSTEMS BANITA CHADHA1, NEHA AGARWAL2 & UMANATH VISHWAKARMA3 1

Assistant Professor, IEC College, Greater Noida, Uttar Pradesh, India 2

Assistant Professor, Amity University, Noida, Uttar Pradesh, India 3

Research Scholar, Amity University, Noida, Uttar Pradesh, India

ABSTRACT Geographic Information Systems Have Been Widely Used in Decision Support Systems as They Provide Opportunities to Overcome Many of Computer Based Models in Terms of Data Preparation, Information Extraction and Visualization. The Main Objective of This Research Is to Understand and Analyze Several GIS Based Decision Support Systems and Then Comparing it With Ecosystem Management Software Provided by ICIMOD Organization. This Task Includes Finding Common Set of Features and Exploring Latest, Efficient and Effective Services of Different Architectures of GIS based Decision Support Systems.

KEYWORDS: GIS, ICIMOD, DSS, SDSS INTRODUCTION Geographic Information Systems (GIS) and Decision Support Systems (DSS) are mechanisms that can be used to provide managers with information needed to make sound resource management decisions [11]. Ecosystem Management in mountainous area has become a challenge for concerned organizations. Better and fast management might ensure their objectives to be achieved successfully. In this context, a GIS based Decision support system might provide a platform where they can handle issues regarding ecosystem in a very efficient way. Many GIS decision support systems have been proposed regarding different fields like watershed management, transportation management etc. Functionalities and services might vary depending on requirements. After studying and analyzing these systems, new functions could be added into previous version of ecosystem management software. Spatial data from different sources integrated within the environment of a GIS has been used in various applications. Users can link data, combine different information, visualize the results and find answers to multiple questions. Digital maps providing a global overview of the smaller or larger scale environment are used as background and help in the visualization of spatial data in revealing relations between them. GIS provides sound functionality for decision support systems. It generally includes visualization models of geographical data and they benefit from the integration of various models for the management of specific information. Hu Yingzhan et al. [1] proposed an automatic monitoring architecture and decision support system for control of water resources. Remote sensing devices have been used for input capturing. Thematic mapping, overlay analysis and spatial data mining have been used to analyze data spatially. Generally in GIS based DSS, spatial and attribute data is stored separately in databases. Rou Guanglei et al. [2] proposed an emergency decision support system which performs qualitative and quantitative analysis of pollution present in environment using Arc Objects Software. Spatial analysis functions like map operation, inquiring and locating sources have been used for analysis purpose.


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BACKGROUND GIS based DSSs are based on a modular architecture that combines multiple applications and technologies. A main application characterized by a unique working environment (graphical user interface) contains the key functionality essential or frequently needed for system to be designed. The other modules communicate with the main application through custom procedures, appropriate intermediate application layers or interchange file formats. Due to technical solutions identified for software development, the structure of designed system might result in separate modules or in form of logical grouping of functionalities within the same module or application. It is most often that there are inter-linkages between the different modules, such as spatial analysis, modeling and scenario analysis, and decision analysis.Spatial analysis is the heart of GIS based DSS as it manages spatial data, both in vector and raster formats. It also provides basic editing functions for point, line and polygon features. The basic classification and overlay operations on vector and raster data are performed through spatial analysis module. Modeling and Scenario Analysis provide the functions to browse and execute the dynamics models of system. It will allow the users to select parameters that influence the behavior of system. System behavior analysis is possible through a set of displays in the form of charts, tables, and maps that can be interactively customized by the user. Decision Analysis is aimed at ranking and selecting the most appropriate options from existing alternatives. Knowledgebase is a meta-data based catalogue containing maps, photos, videos and data sets etc. Advanced spatial analysis provides a facility so that external software engine should be provided to integrate with working decision support software. ICIMOD abbreviated for International Centre for Integrated Mountain Development is a regional knowledge development and learning centre serving the eight regional member countries of the Hindu Kush-Himalayas. ICIMOD is working to develop an economically and environmentally sound mountain ecosystem to improve the living standards of mountain populations and to sustain vital ecosystem services for the billions of people.

GIS FUNCTIONALITY REGARDING DSS A GIS based DSS generally consists of two elements which are CDM (conceptual data model) and a geodatabase for system itself [6]. Conceptual model is converted to geodatabase which is essential for performing GIS functionalities and other analysis to give understandable results. A CDM itself consists of two phases, which are planning, design and creation of CDM. Objects along with attributes are identified in planning and design phase after negotiating with domain experts.In creation of CDM, we actually implement planning and designing phase in the form of UML diagrams. UML diagrams contain objects which are either spatial or non-spatial. This diagram also shows relationships between objects. In geodatabase, we convert objects into GIS.UML diagram along with its objects is transformed into geodatabase which is essential part of GIS system. We also perform land use analysis using satellite images and DEM analysis, which tells us about slope and flow direction which is very useful to analyze different kind of scenarios. From different scenarios, analyst and policy makers can decide which measures or precautions should be taken so that consequences of unexpected incidents could be tackled efficiently and successfully. Rou Guanglei et al. [2] proposed an emergency decision support system which performs qualitative and quantitative analysis of pollution present in environment. Using ArcObjects software, spatial analysis functions like map operation, inquiring and locating sources have been used for analysis purpose. Songhua DSS system is highly suitable for forecasting, risk warning and disaster management [2]. Polluted sites were stored in spatial database and were analyzed by spatial functions of GIS. For implementation purposes VB, VC, ARCGIS were used.


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Policy Decision Support System The main challenging task in a typical SDSS is to maintain link or relationship between databases of different systems and updating it whenever required [9]. In this system, conceptual framework is required to develop or implement things efficiently. Before SDSS there were no connections of tourism laws with GIS but now a general user as well as government officials have connections or accessibility to tourism laws. A database for tourism resources is used to give information about different recreational resources or tourist spots. Its all attributes like location, city etc are stored in this database. In this system, all laws related by tourism are managed by database administrators. General User can not modify or delete any of these laws; it also maintains history of previous investments. In tourism statistics we store the information regarding number of visitors per year etc; which are helpful for new investors. As data related to these databases is strongly connected to location of certain place therefore role of GIS plays very important role as far querying on all these databases is concerned.

Figure 1: Linkage Diagram between GIS and Databases [9] Rain Water Harvesting Systems Rain water harvesting systems are used to increase water availability so that water as resource should be managed efficiently. Input data in the form of vector, layers or themes. These themes may be of rainfall, slope, soil texture, soil depth, drainage or land cover. RWH [3] technologies were ndiva, stone terraces, bench terraces and borders. Each layer has some relative importance with respect to other layer for some technology. RIWs [3] are calculated with the help of pairwise comparison matrix. Suitability levels for a certain technology on a given factor are measured which are scaled from 1 to 9.

Figure 2(a): Elevations [3]


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Banita Chadha, Neha Agarwal & Umanath Vishwakarma

Figure 2(b): Soil Depths [3]

Figure 2(c): Land Cover [3] Suitability level (S) for cell i for stone terrace technology „SSiâ€&#x; is defined as [3],

Differential GPS was used to measure coordinates and elevations very accurately. Aerial photographs and topographic maps were used to extract land cover and drainage patterns.

Figure 3(a): Potential Sites for Bangalala [6]


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Figure 3(b): Potential Sites for Mwembe [6] The proposed Eco-DSS [5] provides different options. Aerial photographs at a scale of 1:65000 are used to identify land use. The number of distinct land covered is five. Visual interpretation helps to categorize it and then these land use are digitized to create thematic layers. Rivers are also digitized to create its thematic layer and then all these thematic layers are combined to identify correct potential sites for rain water harvesting technologies. This system is validated for two different villages named Mwembe and Bangalala so that these results should be compared with each other. Validation of system was done with the help of suitability level and relative importance weights. As Iftikhar Skider [5] has used two different sites to test our results so desired maps along with different elevation model are shown. Results for Mwembe village show that these areas are highly suitable for bench terraces and borders whereas results for Bangalala village show that all rain water harvesting technologies are equally suitable for this area. These results also prove that different harvesting technologies depend on different slope ranges. For example, ndiva technologies are suitable near rivers or streams so slope range for this area is ten to thirty degrees. Environmental Adaptability of Crops The proposed Eco-DSS [5] provides different options to identify right crops or plantation species for a particular environment. In this way, it helps to identify tolerant crops for a given location or identify all locations that match crop requirements. An expert knowledge base ECOCROP is used which contains tolerance limit crops for given environment. Complex spatial problems also considered as semi or ill-structured problems are solved through integration of knowledge based systems and GIS based decision support systems. Integration of knowledge based systems is usually done through three methods which are loosely coupled, tightly coupled and embedded systems. ArcObject as a customization language is used to implement loosely coupled systems. Integration of tightly coupled systems is done through coded module. AML (Arc Macro Language) is used to implement these systems. For these systems, low level access to knowledge based systems is provided. In embedded systems, GIS and knowledge based systems share memory and common interface. All data present in spatial database is raster based. In this way, map algebra could be easily applied on data which provides spatial operators e.g., arithmetic, Boolean etc. and functional operators e.g., local global etc. Major programming has been done in AML (Arc Macro Language) which is high level algorithmic language to give researchers an ability to customize GIS functions. Particular constraints could also be set up by users which affect output results. Predicting Extent of Flood Events A computerized system is designed and implemented that has the ability to predict extent of flood events and shows near real time flood information for general public and decision makers. Floods cause severe damage to flood


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sensitive areas. Specifically areas near to rivers are under direct threat of flood. With the advent of advanced technologies like interpolating digital elevation models, it is now possible to speculate or guess strength of floods. Further these systems could be made accessible to general users by providing on-line web interfaces. In this way, a general user can take necessary measures to minimize loss that happens for unexpected cases.

Figure 4: Flood Scenarios for City [4] In this paper, only the area near to river is concentrated to find out its sensitivity for floods. The proposed flood forecast system utilizes pre-implemented hydrologic modeling software named DWOPER [4] (Dynamic Wave Operational Model) that works on 60 water level gauges. This software uses climatic data, weather forecast data, snow data and flow data as input data and predicts water levels for next 48 hours. This input data is collected from wide variety of telecommunication systems from satellite to telephone. Results of previous 10 years show that predicted water levels have confidence of 95%. CARIS (Computer Aided Resource information System) provides advanced software to manage spatial and non-spatial data. It also supports TINs (Triangulated Irregular Networks) and advanced algorithms for DTMs like interpolating techniques etc. Since available data sets have accuracy limitations so we need accurate DEM data. Firstly TIN is generated from elevation data and then using control points and TINs, comparative surface analysis is used to measure difference. Finally we get accurate control points. A tool named automated floodplain delineation is used to produce floodplain maps. Inputs for this tool are DTM and water levels which help in determining direction and extent of flow over a floodplain area. Major output of this tool is flood plain depth data set which is generated by comparing water surface TIN with a ground surface DTM data.

Figure 5: Floodplain Delineation Process [4]


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Monitoring Management of Resources Hu Yingzhan et al. [1] utilize and integrate many technologies and applications in developing proposed system. These technologies and applications are geographic information system, Remote Sensing, multimedia, computer simulation and other advanced technology means to collect and store the digital information of concerned area. GIS and databases play vital role in functionality of entire system. Multi-sensor data fusion technology has been introduced in this system to collect and monitor the information of water quality and water environment. Proposed system software is composed of four parts which are database design, Model library design, method library design and knowledge library design. Database design component is very important component of system and consists of spatial data and attribute data.

Figure 6: Structure of Model Library [1] Model library design is core component of proposed decision support system and is further divided into four models which are simulation models, forecasting models, optimization models and evaluation models. Method library design provides various algorithms to implement different models. Knowledge library design is very helpful for solving complex decision problems. For implementation, SQL Sever 2005, MapInfo and JBuilder 9.0 has been used in this system. Songhua DSS Rou Guanglei et al. [2] proposed a system which provides technical support to protect and develop sustainable environment for Songhua river basin. This system is capable of monitoring pollution present in environment. ArcObject and VB 6.0 were used to implement this system. ArcObject is capable of providing all basic functions of GIS. A pollution database of Songhua River was designed to visualize the system. Attribute data was organized in the form of relational data model and spatial database was stored in the form of vector data model. Functionality of the system could be divided into four parts: In first part, basic GIS functions like find, zoom, pan etc have been used; In second part, spatial and attribute information is searched by interactive query; In third part, simulation of pollutants diffusion in Songhua river is observed; In last part, emergency measures are taken and disaster is assessed.


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COMPARISION OF DST TOOLBOX WITH GIS BASED DSSS In terms of implementation, this software doesnâ€&#x;t handle graceful exception handling. That is unknown error messages are printed on screen. Resterization and vectorization which are frequently required in a spatial analysis are not provided as a module although these functionalities could be provided through advanced spatial analysis but this is not efficient way. Advanced interpolation techniques like IDW are not bundled with software as a module or functionality. On line map interfaces are very helpful in terms of querying some input data as online map interfaces not only save time of common user but also give facility of quick processing.

TESTING OF DST TOOLBOX Different kinds of testing have been performed for DST tool box implemented for ICIMOD organization [12]. Table 1: Comparison of DSSs with ICIMOD

No No

DSS Monitoring Yes Yes

DSS Songhua Yes Yes

Crops Adapt Yes Yes

No

No

No

Yes

yes

Yes Yes C#, ArcIMS, Google API

Yes Yes SQL Sever 2005, MapInfo and JBuilder 9.0

Features

ICIMOD

Rasterization Vectorization Interpolation for DEMs Integration with external GIS software Map Algebra Digitization Tools used

Yes No

Flood prediction No Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No Yes

Yes No

Yes Yes

Yes No

ArcObject and VB 6.0

ArcObjects, AML

ArcView, ArcInfo

DWOPER, CARIS GIS

RWH

In Black Box testing the internal implementation of the DST is viewed as a black box and the testing engineer only observes the outputs for a given input, (e.g. Use case) and examines if there is a conformity between the result so obtained to the one that is expected thereof. The testing engineer prepares test cases for functional testing but if required can engage in other test that constitute a typical black box testing. As there is no formal Software Requirements Specification document available for the DST, the test cases are based on design document and the User Manual only. In this DST toolbox, Domain testing, usability testing, performance testing and load testing have been conducted to evaluate the system. These results are not up to the mark and need improvement. A comparison with other GIS based decision support systems reveals that many new features could be added to this system for the purpose of enhancement and better efficiency.

CONCLUSIONS A spatial decision support system requires conceptual analysis or domain analysis of system to be designed. After identifying proper objects and their attributes which are obtained from domain experts, this model is transformed into GIS. GIS, domain model and expert knowledge play a vital role while implementing a spatial decision support system. Decision support systems in web-based format can be very helpful in terms of accessibility. Accurate results depend on accurate data which could be obtained by applying DGPS (differential GPS). TINs and advanced interpolating techniques help a lot while processing spatial data. AML (Arc Macro Language) which is a customization language is widely used to edit GIS functions. A good GIS based DSS needs to provide all necessary and latest features that are very helpful to evaluate it. All


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these features and other tools could be added to ICIMOD software to enhance it.

REFERENCES 1.

Hu Yingzhan; Hu Xuemei; Yu Xingzhi. “The Design of Monitoring Management Decision Support System of Water Resources Based on GIS” Intelligent Systems and Applications (ISA), 2011 3rd International Workshop on Issue Date: 28-29 May 2011, page(s): 1 - 4

2.

Rou Guanglei; Zhang Rongyan; “Research on Emergency Decision-Making Supporting System of Songhua River Based on GIS” International Conference on Computer, Mechatronics, Control and Electronic Engineering (CMCE), Issue Date: 24-26 Aug.2010 ,On page(s): 450 – 452

3.

Yie-Ru Chiu; Chao-Hsien Liaw; Hsueh-Hsien Chang. “GIS-based decision supporting system in hydraulic simulation and economic analysis for rainwater harvesting design” Computer Supported Cooperative Work in Design, 2008. CSCWD 2008. 12th International Conference on Volume, Issue , 16-18 April 2008 Page(s):1133 – 1138.

4.

Mioc, D.; Gengsheng Liang; Anton, F.; Nickerson, B.G. “Decision support for flood event prediction and monitoring”Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International Volume, Issue, 23-28 July 2007 Page(s):2439 – 2442.

5. Iftikhar Skider. “Knowledge-based spatial decision support systems: An assessment of environmental adaptability of crops” Expert Systems with Applications: An International Journal Volume 36 , Issue 3 (April 2009) Pages 5341-5347. 6.

Tmbo, Mahoo “GIS-based decision support system for identifying potential sites for rainwater harvesting” Physics and Chemistry of the Earth, Volume 32, Issue 15-18, p. 1074-1081.

7.

Hormdee, D.; Kanarkard, W.; Adams, R.G.; Davey, N.; Taweepworadej “Risk management for chemical emergency system based on GIS and Decision Support System (DSS)” TENCON 2006. 2006 IEEE Region 10 Conference Volume , Issue , 14-17 Nov. 2006 Page(s):1 – 3

8.

Maria Ilinca Alexandrescu 1, Bogdan Cheveresan 2, Simona Catana “GIS Decision Support System for Flood Control in Urban Areas” Technical University of Civil Engineering Bucharest ESRI Romania 3National Administration of Meteorology Romania

9. GIS for a Policy Decision Support in National Tourism Portal” Dr. Dockkey Kim Korea Culture and Tourism Policy Institute Manager Myungshin Chae Public business Team at Ligersystems Co., LTD 10. An Integrated GIS and knowledge based decision support system in assisting farmlevel agronomic decisionmaking” Center of Spatial Information and Digital Engineering, Wuhan University, Hubei Province, China, 430079 Commission VI, WG I 11. “Geographic Information System and Decision Support System” www.fws.gov/midwest/GreatLakes/library/factgisdss 12. International Centre for Integrated Mountain Development http://www.icimod


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