Flood disaster level evaluation using a particle swarm optimization algorithm considering decision maker's preference

Abstract

There has been an increasing demand for accurately evaluating flood disaster levels in order to improve public safety and management of water resources. This article reviews the characteristics of the existing subjective and objective assessment methods, and introduces decision maker's preferences in the particle swarm optimization (PSO) algorithm for a better assessment of flood disaster level. The indicator weight vector of the fuzzy clustering iterative (FCI) model is used as the position vector of a particle in the approach herein proposed. The individuals that do not satisfy the preferences are screened out during the evolution process, in order to obtain more reasonable assessment results according to the preferences for the given scenario. The optimal weight vector of flood disaster samples is then obtained using FCI model combined with the proposed PSO with decision maker's preferences, referred to as PSODP algorithm. A case study of Xinjiang autonomous region of China demonstrates the results of flood rating using different decision maker's preferences, and provides several suggestions on preference type choice. This type of analysis could be used by the water conservation department to better classify the level of a disaster and channel resources accordingly, in the best way possible to manage the situation.