• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1647-1651
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• 2009

The uncertainty or imprecision associated with vague parameters and weighting sets, reduces the ability to decide what alternative is better for a particular location. To efficiently reduce the effect of imprecision frequently arising in available information, fuzzy theory has been used to improve consideration of imprecision in a Multi-Criteria Decision Analysis (MCDA) problem. Fuzzy logic offers a way to represent and handle imprecision present in continuous real world applications. A GIS implementing fuzzy set theory, (referred to in this paper as the "Spatial Fuzzy Approach") enables decision makers to express imprecise concepts associated with geographic data and provides decision makers the ability to have even more definition and discrimination in terms of the best alternatives for a particular spatial location. This study is focused on addressing questions pertaining to the methodology of floodplain analysis using GIS and Spatial Fuzzy MCDA to evaluate flood damage reduction alternatives. The issues will be examined in a case study of the Suyoung River Basin in Pusan, Korea.

• Journal of Korea Water Resources Association
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• v.44 no.12
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• pp.1015-1029
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• 2011

Conventional MCDA techniques have been used in the field of water resources in the past. A GIS can offer an effective spatial data-handling tool that can enhance water resources modeling through interfaces with sophisticated models. However, GIS systems have a limited capability as far as the analysis of the value structure is concerned. The MCDA techniques provide the tools for aggregating the geographical data and the decision maker's preferences into a one-dimensional value for analyzing alternative decisions. In other words, the MCDA allows multiple criteria to be used in deciding upon the best alternatives. The combination of GIS and MCDA capabilities is of critical importance in spatial multi-criteria analysis. The advantage of having spatial data is that it allows the consideration of the unique characteristics at every point. The purpose of this study is to identify, review, and evaluate the performance of a number of conventional MCDA techniques for integration with GIS. Even though there are a number of techniques which have been applied in many fields, this study will only consider the techniques that have been applied in floodplain decision-making problems. Two different methods for multi-criteria evaluation were selected to be integrated with GIS. These two algorithms are Compromise Programming (CP), Spatial Compromise Programming (SCP). The target region for a demonstration application of the methodology was the Suyoung River Basin in Korea.

• Proceedings of the Korea Contents Association Conference
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• 2014.06a
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• pp.357-358
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• 2014

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.798-802
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• 2010

The integration of GIS and fuzzy MCDA(Multi-Criteria Decision Analysis) allows the engineer to determine the preferred alternative for each spatial location in the study area. The next step is to recommend to the final decision makers a single flood management alternative for the entire region. Note that if the study area is large, it might be possible to use the kind of information to recommend different alternatives for different portions of the region. However, for this study it is assumed that only a single alternative will be used. In this study, a "cost of uniformity" metric is proposed that allows decision makers to compute the impact of selecting a single alternative for the entire floodplain. This metric represents the increase in the average distance metric value as compared to the spatially diverse solution from the MCDA and GIS analysis. The results could be applied to any region of the floodplain as desired. Whether the decision makers decide to apply these calculations to the entire floodplain or to specific important regions within the floodplain, an analysis of the increases in the cost of uniformity provides an integrated way for the decision maker to rank the alternatives. This should provide an improvement in their engineering analysis.