| Comparison between the Application Results of NNM and a GIS-based
Decision Support System for Prediction of Ground Level SO<sub>2</sub> Concentration
in a Coastal Area |
| Ok-Hyun Park1†, Min-Gwang Seok2, and Ji-Young Sin3 |
1Department of Environmental Engineering, Pusan National University, Busan 609-735, Korea
2Yoosung Co., Ltd., Ulsan 689-892, Korea
3Hansol EME Co., Ltd., Gyeonggi-do 463-824, Korea |
Corresponding Author:
Ok-Hyun Park ,Tel: +82-51-510-2415, Fax: +82-51-514-9574, Email: ohpark@pusan.ac.kr
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Received: May 7, 2008; Accepted: April 7, 2009. |
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| ABSTRACT |
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A prototype GIS-based decision support system (DSS) was developed by using a database management system (DBMS), a model management
system (MMS), a knowledge-based system (KBS), a graphical user interface (GUI), and a geographical information system (GIS). The method of
selecting a dispersion model or a modeling scheme, originally devised by Park and Seok,1) was developed using our GIS-based DSS. The performances
of candidate models or modeling schemes were evaluated by using a single index(statistical score) derived by applying fuzzy inference to
statistical measures between the measured and predicted concentrations. The fumigation dispersion model performed better than the models such as
industrial source complex short term model(ISCST) and atmospheric dispersion model system(ADMS) for the prediction of the ground level SO<sub>2</sub> (1 hr)
concentration in a coastal area. However, its coincidence level between actual and calculated values was poor. The neural network models were
found to improve the accuracy of predicted ground level SO<sub>2</sub> concentration significantly, compared to the fumigation models. The GIS-based DSS
may serve as a useful tool for selecting the best prediction model, even for complex terrains. |
| Keywords:
Geographical information system | Decision support system | Model selection | Fumigation model | Modeling performance |
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