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Prediction of Landslide Displacement Based on the Variational Mode Decomposition and GWO-SVR Model

Author

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  • Chenhui Wang

    (School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China)

  • Wei Guo

    (Center for Hydrogeology and Environmental Geology Survey, China Geological Survey, Baoding 071051, China)

Abstract

Accurate prediction of landslide displacement is an effective way to reduce the risk of landslide disaster. Under the influence of periodic precipitation and reservoir water level, many landslides in the Three Gorges Reservoir area underwent significant displacement deformation, showing a similar step-like deformation curve. Given the nonlinear characteristics of landslide displacement, a prediction model is established in this study according to the variational mode decomposition (VMD) and support vector regression (SVR) optimized by gray wolf optimizer (GWO-SVR). First, the original data are decomposed into trend, periodic and random components by VMD. Then, appropriate influential factors are selected using the grey relational degree analysis (GRDA) method for constructing the input training data set. Finally, the sum of the three displacement components is superimposed as the total displacement of the landslide, and the feasibility of the model is subsequently tested. Taking the Shuizhuyuan landslide in the Three Gorges Reservoir area as an example, the accuracy of the model is verified using the long time-series monitoring data. The results indicate that the newly proposed model achieves a relatively good prediction accuracy with data decomposition and parameter optimization. Therefore, this model can be used for the predict the accuracy of names and affiliations ion of landslide displacement in the Three Gorges Reservoir area.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:5470-:d:1102500
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    References listed on IDEAS

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    1. Chonghao Zhu & Jianjing Zhang & Yang Liu & Donghua Ma & Mengfang Li & Bo Xiang, 2020. "Comparison of GA-BP and PSO-BP neural network models with initial BP model for rainfall-induced landslides risk assessment in regional scale: a case study in Sichuan, 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. 100(1), pages 173-204, January.
    2. 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.
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    Cited by:

    1. Longye Hu & Chaode Yan, 2024. "Evaluation of Landslide Susceptibility of Mangshan Mountain in Zhengzhou Based on GWO-1D CNN Model," Sustainability, MDPI, vol. 16(12), pages 1-23, June.
    2. Xuebin Xie & Yingling Huang, 2024. "Displacement Prediction Method for Bank Landslide Based on SSA-VMD and LSTM Model," Mathematics, MDPI, vol. 12(7), pages 1-17, March.

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