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A multi-criteria decision-making model dealing with correlation among criteria for reservoir flood control operation
Author(s) -
Feilin Zhu,
Pingan Zhong,
Bin Xu,
Yenan Wu,
Yu Zhang
Publication year - 2015
Publication title -
journal of hydroinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.654
H-Index - 50
eISSN - 1465-1734
pISSN - 1464-7141
DOI - 10.2166/hydro.2015.055
Subject(s) - multiple criteria decision analysis , flood control , topsis , principal component analysis , ranking (information retrieval) , computer science , entropy (arrow of time) , data mining , curse of dimensionality , weight , cascade , mathematical optimization , flood myth , operations research , mathematics , engineering , artificial intelligence , geography , physics , archaeology , quantum mechanics , lie algebra , pure mathematics , chemical engineering
Flood control operation in a multi-reservoir system is a multi-criteria decision-making (MCDM) problem, in which the considered criteria are often correlated with each other. In this paper, we propose an MCDM model for reservoir flood control operation to deal with correlation among criteria. Considering the flood control safety of reservoirs and downstream protected regions, we establish the hierarchical structure of the criterion system. We use the principal component analysis method to eliminate the correlation, and transform the original criterion system into an independent comprehensive criterion system. The comprehensive decision matrix coupled with the weight vector obtained by the improved entropy weight method serves as the input to TOPSIS method, fuzzy optimum method, and fuzzy matter-element method, by which we determine the ranking order of the alternatives. We apply the proposed model to a cascade system of reservoirs at the Daduhe River basin in China. The results show that the dimensionality of the criterion system is reduced and the correlation among criteria is eliminated simultaneously, and the ranking order of the alternatives is reasonable. The proposed model provides an effective way to deal with correlation among criteria, and can be extended to wider applications in many other MCDM problems.

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