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Drought prediction in the Yunnan–Guizhou Plateau of China by coupling the estimation of distribution algorithm and the extreme learning machine

Author

Listed:
  • Qiongfang Li

    (Hohai University
    Hohai University)

  • Yao Du

    (Hohai University)

  • Zhennan Liu

    (Guizhou Institute of Technology, Guizhou University)

  • Zhengmo Zhou

    (Hohai University)

  • Guobin Lu

    (Hohai University)

  • Qihui Chen

    (Hohai University)

Abstract

Drought prediction is a critical non-engineering approach to mitigate their significant threats to water availability, food safety, and ecosystem health. Therefore, to improve the efficiency and accuracy of drought prediction, a novel drought prediction model was proposed by optimizing the extreme learning machine (ELM) using the estimation of distribution algorithm (EDA) (EDA-ELM) and evaluated by the comparison with the genetic algorithm-optimized ELM (GA-ELM) model, standard ELM model, and adaptive network-based fuzzy inference system (ANFIS) in drought prediction for Yunnan–Guizhou Plateau (YGP). The standardized precipitation evapotranspiration index (SPEI) in 3/6/12-month time scales was treated as the dependent variable and the primary drought driving factors as predictor variables. The results revealed that the EDA-ELM model performed best in multiscalar SPEI prediction, followed by GA-ELM, ANFIS, and standard ELM models, while the model execution time was descended by EDA-ELM, GA-ELM, ANFIS, and standard ELM models, varying from 100 to 700 s. The outputs could provide a novel approach to drought prediction and benefit drought prevention and mitigation.

Suggested Citation

  • Qiongfang Li & Yao Du & Zhennan Liu & Zhengmo Zhou & Guobin Lu & Qihui Chen, 2022. "Drought prediction in the Yunnan–Guizhou Plateau of China by coupling the estimation of distribution algorithm and the extreme learning machine," 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. 113(3), pages 1635-1661, September.
    • Handle: RePEc:spr:nathaz:v:113:y:2022:i:3:d:10.1007_s11069-022-05361-4
    DOI: 10.1007/s11069-022-05361-4
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    References listed on IDEAS

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    1. Gao, Shujun & de Silva, Clarence W., 2018. "Estimation distribution algorithms on constrained optimization problems," Applied Mathematics and Computation, Elsevier, vol. 339(C), pages 323-345.
    2. Ziqi Yan & Yapeng Zhang & Zuhao Zhou & Ning Han, 2017. "The spatio-temporal variability of droughts using the standardized precipitation index in Yunnan, China," 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. 88(2), pages 1023-1042, September.
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