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.
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Abbreviations
- ANFIS:
-
Adaptive network-based fuzzy inference system
- ANN:
-
Artificial neural network
- CORR:
-
Correlation coefficient
- \({D}_{O}\)/\({D}_{p}\) :
-
Observed/predicted drought duration
- EDA:
-
Estimation of distribution algorithm
- EDA-ELM:
-
Estimation of distribution algorithm-optimized extreme learning machine
- ELM:
-
Extreme learning machine
- ENSO:
-
El Niño and the Southern Oscillation
- \({E}_\text{NS}\) :
-
Nash–Sutcliffe coefficient
- GA:
-
Genetic algorithm
- GA-ELM:
-
Genetic algorithm-optimized ELM
- MAE:
-
Mean absolute error
- RMSE:
-
Root mean square error
- \({\text{RE}}_{S}\)/\({\text{RE}}_{D}\) :
-
Relative errors of drought severity and duration
- SLFN:
-
Single-hidden-layer feedforward neural network
- SPEI:
-
Standardized Precipitation Evapotranspiration Index
- \({S}_{O}\)/\({S}_{p}\) :
-
Observed/predicted drought severity
- WI:
-
Willmott’s index
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Financial support is gratefully acknowledged from the National Natural Science Foundation Commission of China under Grant numbers 51879069 and 41961134003, Guizhou Science Foundation-ZK [2021] General 295 and the Jiangsu Provincial Collaborative Innovation Center of World Water Valley and Water ecological civilization, China.
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Yao Du and Zhennan Liu involved in conceptualization, methodology, software. Qiongfang Li took part in data curation, writing—original draft preparation. Zhengmo Zhou and Guobin Lu involved in writing—reviewing and editing. Qihui Chen took part in software, validation.
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Li, Q., Du, Y., Liu, Z. et al. Drought prediction in the Yunnan–Guizhou Plateau of China by coupling the estimation of distribution algorithm and the extreme learning machine. Nat Hazards 113, 1635–1661 (2022). https://doi.org/10.1007/s11069-022-05361-4
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DOI: https://doi.org/10.1007/s11069-022-05361-4