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Background <p> Following the impoundment of the Three Gorges Reservoir, deposit-layer landslides in the reservoir basin have experienced cyclic deformation under the combined influence of seasonal precipitation and periodic water-level operations. The deformation amplitude accumulates annually, continuously bringing uncertainty to the safety of the reservoir basin.</p> Purpose <p> This study takes the Tanjiahe landslide (a typical feature in the Three Gorges Reservoir region) as the research object. By integrating its recent daily displacement measurements, daily rainfall data, and reservoir water-level observations, this research aims to establish a deep learning-based prediction model to forecast the landslide’s long-term deformation trends. Specifically, the aggregated displacement time series is segmented into a trend term and a cyclic term, so as to separately characterize the landslide’s long-term evolution and periodic dynamic behavior.</p> Methods <p> First, Grey Relational Analysis (GRA) and Pearson’s correlation analysis are used to identify the dominant influencing factors of the Tanjiahe landslide (including antecedent rainfall, reservoir water-level variation characteristics, and past displacement increments) as input features. Then, considering the timeseries nature of monitoring data and limited sample size constraints, a Long Short-Term Memory (LSTM) network is developed to predict the landslide’s cyclic displacement, while the trend component is modeled via Support Vector Machine (SVM) regression.</p> Results <p> The results indicate that the LSTM method significantly outperforms in depicting the complex nonlinear time-dependent features of landslide deformation; the SVM method stably captures the aggregate deformation trajectory and shows strong performance in low-frequency trend regression. Additionally, Rescaled Range (R/S) analysis of observed versus predicted sequences reveals significant long-term persistence in the Tanjiahe landslide’s deformation evolution at its current phase.</p> Conclusion <p> This study demonstrates that the combined LSTM-SVM model can effectively predict the Tanjiahe landslide’s long-term deformation. The identified long-term persistence of deformation provides a basis for long-term risk assessment and early warning of this landslide.</p>

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Deformation trend prediction for hydraulically driven landslides in reservoir areas: modeling from high temporal resolution data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月29日

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  • Xiaolin Fu,
  • Yanjun Zhang,
  • Xiaodong Fu,
  • Wenjie Du,
  • Runqing Ye

摘要

Background

Following the impoundment of the Three Gorges Reservoir, deposit-layer landslides in the reservoir basin have experienced cyclic deformation under the combined influence of seasonal precipitation and periodic water-level operations. The deformation amplitude accumulates annually, continuously bringing uncertainty to the safety of the reservoir basin.

Purpose

This study takes the Tanjiahe landslide (a typical feature in the Three Gorges Reservoir region) as the research object. By integrating its recent daily displacement measurements, daily rainfall data, and reservoir water-level observations, this research aims to establish a deep learning-based prediction model to forecast the landslide’s long-term deformation trends. Specifically, the aggregated displacement time series is segmented into a trend term and a cyclic term, so as to separately characterize the landslide’s long-term evolution and periodic dynamic behavior.

Methods

First, Grey Relational Analysis (GRA) and Pearson’s correlation analysis are used to identify the dominant influencing factors of the Tanjiahe landslide (including antecedent rainfall, reservoir water-level variation characteristics, and past displacement increments) as input features. Then, considering the timeseries nature of monitoring data and limited sample size constraints, a Long Short-Term Memory (LSTM) network is developed to predict the landslide’s cyclic displacement, while the trend component is modeled via Support Vector Machine (SVM) regression.

Results

The results indicate that the LSTM method significantly outperforms in depicting the complex nonlinear time-dependent features of landslide deformation; the SVM method stably captures the aggregate deformation trajectory and shows strong performance in low-frequency trend regression. Additionally, Rescaled Range (R/S) analysis of observed versus predicted sequences reveals significant long-term persistence in the Tanjiahe landslide’s deformation evolution at its current phase.

Conclusion

This study demonstrates that the combined LSTM-SVM model can effectively predict the Tanjiahe landslide’s long-term deformation. The identified long-term persistence of deformation provides a basis for long-term risk assessment and early warning of this landslide.