Flood disaster fuzzy clustering iterative assessment model considering preference information of decision-makers

Decision maker's preference has an important impact on the outcome of the flood disaster assessment. Traditional assessment methods may appear secondary index weight is too large. It is difficult to effectively reflect decision maker's preference on the evaluation result. Hence, the fuzzy clustering iterative (FCI) model is extended to consider the preference of decision-makers, then the theoretical solution under a single preference condition has been given, and the fuzzy clustering iterative model considering preference information of Decision-makers is presented. By means of adding a relaxation factor to construct an augmented Lagrange multiplier into the objective function of FCI model, the inequality constraints that embody the decision maker's preference can be translated into the equality constraints, then the analytic solution in this preference condition can be obtained, and proposes the theory solution for studies of evaluation methods considering the decision maker's preference. Then, the comprehensive evaluation index of each sample can be obtained according to the characteristic value matrix of the floods sample and the solved optimal weight. The levels of each sample are determined combined with optimal subordinate degree matrix, and the ranking in each level is achieved in accordance with the comprehensive evaluation index. The model uses the characteristic value matrix of the floods sample directly to identify the extent of disaster effects, and reduces the dependency on the optimal subordinate degree matrix, thus it gives a more accurate and reasonable ranking result. Finally, the proposed model is applied to analyze the flood data of Sichuan province in 2013, the result verifies that it can help us achieve a outcome of the flood disasters assessment that consider decision maker's preferences and it gives more reasonable ranking result than the traditional classificatory eigenvalue method.