The target of flood hazard assessment is to establish appropriate casualty evaluation models for managing flood and alleviating loss affected by flood. Existing challenges on evaluating flood hazard level focus on how to achieve a stable and high-resolution result according to the incompatibilities between multiple attribute indicators. Base on the chaotic optimization theory, this study presents a chaotic particle swarm optimization (CPSO) algorithm to deal with a fuzzy clustering iterative model (CPSO-FCI) for evaluating flood hazards. By using a celebrated logistic chaotic map and a penalty function, the target function can be tackled more perfectly. The effectiveness of the novel hybrid method is evaluated by three representative test functions and two sets of practical flood disaster samples in China. Simulation results and comparisons show that the presented CPSO based on the logistic map is competitive and stable in performance with standard particle swarm optimization (PSO) and other advanced PSO-type approaches introduced in the literature. High resolution continuous flood rating results are obtained for fine-tune flood management.
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This paper is funded by the National Natural Science Foundation (No.71771073), the Fundamental Research Funds for the Central Universities (PA2020GDKC0006), the CRSRI Open Research Program (Program SN CKWV2017525/KY), and the Open Research Fund of State Key Laboratory of simulation and Regulation of Water Cycle in River Basin (China Institute of Water Resources and Hydropower Research) (Grant NO IWHR-SKL-201605).
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He, Y., Wan, J. Flood hazard assessment based on fuzzy clustering iterative model and chaotic particle swarm optimization. J Ambient Intell Human Comput (2020). https://doi.org/10.1007/s12652-020-02109-5
- Flood hazard assessment
- Evolutionary computation
- Particle Swarm Optimization (PSO)
- Fuzzy clustering Iterative (FCI)
- Chaotic map