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

Qiongfang Li (), Yao Du (), Zhennan Liu, Zhengmo Zhou, Guobin Lu and Qihui Chen
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Qiongfang Li: 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

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2022, vol. 113, issue 3, No 10, 1635-1661

Abstract: 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.

Keywords: EDA-ELM model; GA-ELM model; ANFIS model; Drought prediction; The Yunnan–Guizhou Plateau (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11069-022-05361-4

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