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Adaptive hybrid machine learning model for forecasting the step-like displacement of reservoir colluvial landslides: a case study in the three Gorges reservoir area, China

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Abstract

Establishing an accurate and dependable displacement prediction model is essential to building an early warning system for landslide hazards. This study proposes an adaptive hybrid machine learning model for forecasting the step-like displacements of reservoir colluvial landslides. Firstly, candidate factors are determined based on the landslide deformation response. Then, the cumulative displacement and candidate factors are decomposed using the optimized variational mode decomposition algorithm. Second, the sensitivity analysis of the gray wolf optimizer-based kernel extreme learning machine (GWO-KELM) models to each factor component is analyzed using the PAWN method. Then, the factors are optimized based on the analysis results. Third, based on the optimized factors, GWO-KELM models of different displacement components are established and integrated to predict the cumulative displacement. The Baishuihe landslide was taken as an example. The raw data of its three monitoring sites were employed to verify the performance of the proposed model. The results indicate that the model can decompose the cumulative displacement and factors with the adaptively determined parameters. In addition, the model performed well over a three-year prediction of the landslide displacement.

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Acknowledgements

The authors thank the colleagues in our laboratory for their constructive comments and assistance.

Funding

The study is funded by the National Natural Science Foundation of China (No. 41977244), the Doctoral Research Foundation of Guizhou University (GDRJH 2021 [25]), and the Project of Guizhou Provincial Department of Science and Technology (Guizhou science and technology cooperation platform for talents [2021] 5626).

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Authors and Affiliations

  1. College of Resources and Environmental Engineering, Key Laboratory of Karst Georesources and Environment Ministry of Education, Guizhou University, No. 2708, Huaxi Road, Guiyang, 500025, People’s Republic of China

    Li Linwei, Li Bo & Deng Ziqiang

  2. Faculty of Engineering, China University of Geosciences, Wuhan, 430074, China

    Wu Yiping & Miao Fasheng

  3. Guiyang Engineering Corporation Limited of Power China, Guiyang, 550081, China

    Huang Yepiao

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  1. Li Linwei
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  2. Wu Yiping
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  3. Huang Yepiao
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  4. Li Bo
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  5. Miao Fasheng
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  6. Deng Ziqiang
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Contributions

Li Linwei and Wu Yiping contributed to the conception of the study and wrote the manuscript. Li Linwei and Huang Yepiao performed the experiment and the data analyses. Li Bo and Miao Fasheng contributed significantly to the analysis and manuscript preparation. Deng Ziqiang assisted Li Linwei in revising the manuscript and polishing the language. All authors have read and approved the final manuscript.

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Correspondence to Li Linwei.

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Linwei, L., Yiping, W., Yepiao, H. et al. Adaptive hybrid machine learning model for forecasting the step-like displacement of reservoir colluvial landslides: a case study in the three Gorges reservoir area, China. Stoch Environ Res Risk Assess 37, 903–923 (2023). https://doi.org/10.1007/s00477-022-02322-y

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