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Forecasting reservoir-induced landslide deformation using genetic algorithm enhanced multivariate Taylor series Kalman filter

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Abstract

Given the uncertain nature of reservoir-induced landslides, developing a relatively simple forecast model with high accuracy and strong robustness is still a challenge. This paper improves the classic Kalman filter to the genetic algorithm enhanced multivariate Taylor series Kalman filter (GMT-KF) model, which takes into account the rainfall, the variation of reservoir water-level, and lagging effect. Taking the Outang landslide in the Three Gorges Reservoir of China as an example, the time series data of three GPS monitoring points and two monitoring boreholes were compared with their predicted time series obtained from the GMT-KF model. The results show that the GMT-KF model attains both high accuracy and strong robustness. Moreover, the non-training nature makes the GMT-KF model holds much potential of satisfying the early-warning requirement of the emergent deployment of automatic monitoring systems at the reservoir-induced landslide sites.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their helpful comments and suggestions. This study was financially supported by the National Key R & D Program of China (grant no. 2018YFC1505104), the National Science Foundation of China (grant no. 42077232), and the Science and Technology Project 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

    Ke Liao, Wei Zhang, Hong-hu Zhu, Bin Shi, Jing-tao Wang & Wen-tao Xu

  2. College of Geology Engineering and Geomatics, Chang’an University, Xi’an 710054, China

    Qin Zhang

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  1. Ke Liao
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  2. Wei Zhang
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  3. Hong-hu Zhu
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  4. Qin Zhang
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  5. Bin Shi
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  7. Wen-tao Xu
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Correspondence to Wei Zhang.

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Liao, K., Zhang, W., Zhu, Hh. et al. Forecasting reservoir-induced landslide deformation using genetic algorithm enhanced multivariate Taylor series Kalman filter. Bull Eng Geol Environ 81, 104 (2022). https://doi.org/10.1007/s10064-022-02595-1

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