Flood disaster evaluation based on adaptive fuzzy clustering iterative model and hybrid differential evolution algorithm

Abstract

Abstract In order to reasonably and rapidly evaluate flood disaster, based on a fuzzy clustering iterative model (FCI) and differential evolution algorithm (DE), an adaptive fuzzy clustering iterative model using a hybrid differential evolution algorithm (AFCI-HDE) is proposed, which has three advantages: firstly, the decision-maker's subjective preference was considered to flexibly modify the objective function; secondly, HDE was introduced to optimize the index weight vector of AFCI; thirdly, the validity of its clustering effect was more credible than that of FCI. Finally, the case study revealed that AFCI-HDE is feasible and effective by comparing the optimal fitness and clustering validity values with other approaches, which could reflect various decision-maker's preferences by simple adaptive adjustments and rapidly obtain reasonable evaluation results, thus providing a new effective approach in flood risk management.