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Flood Season Division Model Based on Goose Optimization Algorithm–Minimum Deviation Combination Weighting

Author

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  • Yukai Wang

    (School of Environmental Science and Engineering, Hainan University, Haikou 570228, China)

  • Jun Li

    (School of Ecology, Hainan University, Haikou 570228, China)

  • Jing Fu

    (Industry-University-Research Cooperation Office, Hainan College of Economics and Business, Haikou 571127, China)

Abstract

The division of the flood season is of great significance for the precise operation of water conservancy projects, flood control and disaster reduction, and the rational allocation of water resources, alleviating the contradiction of the uneven spatial and temporal distribution of water resources. The single weighting method can only determine the weight of the flood season division indicators from a certain perspective and cannot comprehensively reflect the time-series attributes of the indicators. This study proposes a Flood Season Division Model based on the Goose Optimization Algorithm and Minimum Deviation Combined Weighting (FSDGOAMDCW). The model uses the Goose Optimization Algorithm (GOA) to solve the Minimum Deviation Combination model, integrating weights from two subjective methods (Expert Scoring and G1) and three objective methods (Entropy Weight, CV, and CRITIC). Combined with the Set Pair Analysis Method (SPAM), it realizes comprehensive flood season division. Based on daily precipitation data of the Nandujiang River (1961–2022), the study determines its flood season from 1 May to 30 October. Comparisons show that: ① GOA converges faster than the Genetic Algorithm, stabilizing at T = 5 and achieving full convergence at T = 24; and ② The model’s division results have the smallest Intra-Class Differences, avoiding indistinguishability between flood and non-flood seasons under special conditions. This research aims to support flood season division studies in tropical islands.

Suggested Citation

  • Yukai Wang & Jun Li & Jing Fu, 2025. "Flood Season Division Model Based on Goose Optimization Algorithm–Minimum Deviation Combination Weighting," Sustainability, MDPI, vol. 17(15), pages 1-19, July.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:15:p:6968-:d:1714605
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    References listed on IDEAS

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    1. Abhishek Ghosh & Shyamal Kumar Kar, 2018. "Application of analytical hierarchy process (AHP) for flood risk assessment: a case study in Malda district of West Bengal, India," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 94(1), pages 349-368, October.
    2. Zhenyu Mu & Xueshan Ai & Jie Ding & Kui Huang & Senlin Chen & Jiajun Guo & Zuo Dong, 2022. "Risk Analysis of Dynamic Water Level Setting of Reservoir in Flood Season Based on Multi-index," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(9), pages 3067-3086, July.
    3. Abhishek Ghosh & Shyamal Kumar Kar, 2018. "Correction to: Application of analytical hierarchy process (AHP) for flood risk assessment: a case study in Malda district of West Bengal, India," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 94(1), pages 369-369, October.
    4. Yuxin Zhu & Dazuo Tian & Feng Yan, 2020. "Effectiveness of Entropy Weight Method in Decision-Making," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-5, March.
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