Abstract
Landslides are one of the most destructive geological disasters and have been caused many casualties and economic losses every year in the world. The reservoir area formed by the world's largest hydropower project, Three Gorges Hydropower project of China, has become a natural testing ground for landslide prediction in the hope of reducing losses. In this paper, a new algorithm with strong optimization ability, the water cycle algorithm (WCA), is combined with the extreme learning machine (ELM) to improve the prediction accuracy of step-wise landslide. The gray relational grade analysis method was adopted to determine the main influencing factors of the landslide's periodic displacement. Then, the determined factors were used as the input items of the proposed WCA-ELM model, and the corresponding periodic displacement was used as the model output item. Taking the Liujiabao landslide in the Three Gorges Reservoir area as a case history, the proposed model was verified through a comparison with the measurements. The results showed that the model has a faster convergence rate and higher prediction accuracy than the traditional back-propagation neural network model and ELM-model. The water cycle algorithm is suitable for optimizing the accuracy of the extreme learning machine model in landslide prediction.
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The authors appreciate the financial support provided by the Fundamental Research Funds for the Central Universities [No.2015XKMS035].
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YZ helped in conceptualization and resources; XC contributed to data curation; LW and YZ helped in methodology; ZS helped in software; RL and ZY wrote—original draft; all authors have read and agreed to the published version of the manuscript.
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Zhang, Yg., Chen, Xq., Liao, Rp. et al. Research on displacement prediction of step-type landslide under the influence of various environmental factors based on intelligent WCA-ELM in the Three Gorges Reservoir area. Nat Hazards 107, 1709–1729 (2021). https://doi.org/10.1007/s11069-021-04655-3
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DOI: https://doi.org/10.1007/s11069-021-04655-3