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Landslide Displacement Prediction Using Kernel Extreme Learning Machine with Harris Hawk Optimization Based on Variational Mode Decomposition

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  • Chenhui Wang

    (Technology Innovation Center for Geological Environment Monitoring, MNR, Baoding 071051, China
    Center for Hydrogeology and Environmental Geology Survey, China Geology Survey, Baoding 071051, China)

  • Gaocong Lin

    (Technology Innovation Center for Geological Environment Monitoring, MNR, Baoding 071051, China
    Center for Hydrogeology and Environmental Geology Survey, China Geology Survey, Baoding 071051, China)

  • Cuiqiong Zhou

    (Key Laboratory of Geohazard Forecast and Geoecological Restoration in Plateau Mountainous Area, MNR, Kunming 650216, China
    Yunnan Key Laboratory of Geohazard Forecast and Geoecological Restoration in Plateau Mountainous Area, Kunming 650216, China
    Yunnan Institute of Geo-Environment Monitoring, Kunming 650216, China)

  • Wei Guo

    (Technology Innovation Center for Geological Environment Monitoring, MNR, Baoding 071051, China
    Center for Hydrogeology and Environmental Geology Survey, China Geology Survey, Baoding 071051, China)

  • Qingjia Meng

    (Technology Innovation Center for Geological Environment Monitoring, MNR, Baoding 071051, China
    Center for Hydrogeology and Environmental Geology Survey, China Geology Survey, Baoding 071051, China)

Abstract

Displacement deformation prediction is critical for landslide disaster monitoring, as a good landslide displacement prediction system helps reduce property losses and casualties. Landslides in the Three Gorges Reservoir Area (TGRA) are affected by precipitation and fluctuations in reservoir water level, and displacement deformation shows a step-like curve. Landslide displacement in TGRA is related to its geology and is affected by external factors. Hence, this study proposes a novel landslide displacement prediction model based on variational mode decomposition (VMD) and a Harris Hawk optimized kernel extreme learning machine (HHO-KELM). Specifically, VMD decomposes the measured displacement into trend, periodic, and random components. Then, the influencing factors are also decomposed into periodic and random components. The feature data, with periodic and random data, are input into the training set, and the trend, periodic, and random term components are predicted by HHO-KELM, respectively. Finally, the total predicted displacement is calculated by summing the predicted values of the three components. The accuracy and effectiveness of the prediction model are tested on the Shuizhuyuan landslide in the TGRA, with the results demonstrating that the new model provides satisfactory prediction accuracy without complex parameter settings. Therefore, under the premise of VMD effectively decomposing displacement data, combined with the global optimization ability of the HHO heuristic algorithm and the fast-learning ability of KELM, HHO-KELM can be used for displacement prediction of step-like landslides in the TGRA.

Suggested Citation

  • Chenhui Wang & Gaocong Lin & Cuiqiong Zhou & Wei Guo & Qingjia Meng, 2024. "Landslide Displacement Prediction Using Kernel Extreme Learning Machine with Harris Hawk Optimization Based on Variational Mode Decomposition," Land, MDPI, vol. 13(10), pages 1-17, October.
  • Handle: RePEc:gam:jlands:v:13:y:2024:i:10:p:1724-:d:1503157
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    References listed on IDEAS

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    1. Qing Ling & Qin Zhang & Jing Zhang & Lingjie Kong & Weiqi Zhang & Li Zhu, 2021. "Prediction of landslide displacement using multi-kernel extreme learning machine and maximum information coefficient based on variational mode decomposition: a case study in Shaanxi, 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. 108(1), pages 925-946, August.
    2. Tommaso CarlĂ  & Emanuele Intrieri & Federico Traglia & Nicola Casagli, 2016. "A statistical-based approach for determining the intensity of unrest phases at Stromboli volcano (Southern Italy) using one-step-ahead forecasts of displacement time series," 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. 84(1), pages 669-683, October.
    3. Chenhui Wang & Wei Guo, 2023. "Prediction of Landslide Displacement Based on the Variational Mode Decomposition and GWO-SVR Model," Sustainability, MDPI, vol. 15(6), pages 1-18, March.
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