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Deformation prediction of reservoir landslides based on a Bayesian optimized random forest-combined Kalman filter

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Abstract

Prediction model plays an important role in the early warning of reservoir landslides. This paper proposes a novel synthetic prediction model, the Bayesian optimized random forest-combined Kalman filter (BORF-KF) in which the Kalman filter and random forest algorithm are used to predict the trend and periodic displacements of the cumulative landslide displacement, respectively. To improve the accuracy of the model, the Bayesian algorithm is used to optimize the parameters, and periodic changes of rainfall and reservoir water level are considered. The applicability, efficiency, and accuracy of the proposed prediction model is successfully verified against monitoring time series obtained from the Outang landslide, a giant resurrected ancient landslide in the Three Gorges reservoir of China. The results show the ground deformation of the reservoir landslide exhibits notable step-like characteristics, which has strong correlation with the concentrated rainfall and the decrease of the reservoir water level. Moreover, predicted cumulative displacement error is less than 2%, suggesting the BORF-KF model attains a high prediction accuracy and can be applied to the prediction of reservoir landslides with abrupt step-like sections.

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Acknowledgements

The authors would like to thank the editors and anonymous reviewers for their helpful comments and suggestions.

Funding

This study was financially supported by the National Key R & D Program of China (Grant no. 2018YFC1505104), the National Science Foundation of China (Grant nos. 42077232, 42077235), and the Science and Technology Foundation of Suzhou City (Grant no. SYG202132).

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Authors and Affiliations

  1. School of Earth Sciences and Engineering, Nanjing University, Nanjing, 210023, China

    Nanfang Zhang, Wei Zhang, Ke Liao, Hong-hu Zhu, Qi Li & Jingtao Wang

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  1. Nanfang Zhang
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Correspondence to Wei Zhang.

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Zhang, N., Zhang, W., Liao, K. et al. Deformation prediction of reservoir landslides based on a Bayesian optimized random forest-combined Kalman filter. Environ Earth Sci 81, 197 (2022). https://doi.org/10.1007/s12665-022-10317-9

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