Prioritization of flood risk based on multi-criteria decision-making models (Gray systems theory-ELECTRE-TOPSIS)

Document Type : Research Article

Authors

1 MA Student, Faculty of Natural Resources, University of Tehran, Iran

2 Associate Professor, Faculty of Natural Resources, University of Tehran, Iran

Abstract

In recent years, flood control projects have been more extended in the country. Given the cost of implementation, prioritizing the sub-basins is of great importance and making decisions about flood and integrated watershed management is quite necessary.The first and most important step in the runoff flood management projects is prioritizing essential areas to carry out flood control projects. Decision-making methods provide effective tools for dealing with issues involving more than one goal. In this study, Parchin watershed basin was studied. Then, using multi-criteria decision-making methods (ELECTRE, Grey and TOPSIS), the sub-basins were prioritized.The results of Grey method were investigated by gray relational analysis for the score of 9 sub-basins. The sub-basin 7 with the highest score (0.719443) was prioritized as the first, and sub-basin 1 with score of (0.466119) was determined to be the second priority and, sub-basin 4 with the lowest score value (.331493) was introduced as the last priority.In TOPSIS method, rating was done using calculated nearness to ideal solution options, sub-basin 7 with the highest nearness coefficient (0.843721) is ranked first. The results from rating showed that all three methods were evaluated equally in first priority; however, other priority methods gave different results. According to the above-mentioned results, the three TOPSIS methods and gray analysis are more similar and more accurate than the electronic method.

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Iranian journal of Ecohydrology
Volume 4, Issue 2
June 2017
Pages 499-508

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History

  • Receive Date: 30 December 2016
  • Revise Date: 10 February 2017
  • Accept Date: 15 March 2017
  • First Publish Date: 22 June 2017
  • Publish Date: 22 June 2017

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