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Open AccessArticle

Flood Classification Based on a Fuzzy Clustering Iteration Model with Combined Weight and an Immune Grey Wolf Optimizer Algorithm

1,2, 2,3,4,*, 1 and 5
1
Changjiang Institute of Survey, Planning, Design and Research, Wuhan 430010, China
2
Key Laboratory of Ecological Remediation for Lakes and Rivers and Algal Utilization of Hubei Province, Hubei University of Technology, Wuhan 430068, China
3
School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
4
Department of Biological and Agricultural Engineering & Zachry Department of Civil Engineering, Texas A&M University, College Station, TX 77843-2117, USA
5
College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Water 2019, 11(1), 80; https://doi.org/10.3390/w11010080
Received: 17 November 2018 / Revised: 11 December 2018 / Accepted: 28 December 2018 / Published: 4 January 2019
(This article belongs to the Special Issue Integrated Flood Management: Concepts, Methods, Tools and Results)
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Abstract

Flood classification is an important basis for flood forecasting, flood risk identification, flood real-time scheduling, and flood resource utilization. However, flood classification results may be not reasonable due to uncertainty, the fuzziness of evaluation indices, and the demerit of not comprehensively considering the index weight. In this paper, based on the fuzzy clustering iterative model, a sensitivity coefficient was applied to combine the subjective and objective weights into a combined weight, then the fuzzy clustering iterative model with combined weight (FCI-CW) was proposed for flood classification. Moreover, an immune grey wolf optimizer algorithm (IGWO) based on the standard grey wolf optimizer algorithm and an immune clone selection operator was proposed for the global search of the optimal fuzzy clustering center and the sensitivity coefficient of FCI-CW. Finally, simulation results at Nanjing station and Yichang station demonstrate that the proposed methodology, i.e., FCI-CW combined with IGWO, is reasonable and reliable, can effectively deal with flood classification problems with better fitness and a comprehensive consideration of the subjective and objective aspects, and has great application potential in sorting, evaluation, and decision-making problems without evaluation criteria. View Full-Text
Keywords: flood classification; FCI; combined weight; GWO; immune clone selection operator flood classification; FCI; combined weight; GWO; immune clone selection operator
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Zou, Q.; Liao, L.; Ding, Y.; Qin, H. Flood Classification Based on a Fuzzy Clustering Iteration Model with Combined Weight and an Immune Grey Wolf Optimizer Algorithm. Water 2019, 11, 80.

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