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Combining Numerical Simulation and Deep Learning for Landslide Displacement Prediction: An Attempt to Expand the Deep Learning Dataset

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  • Wenhan Xu

    (Technology Innovation Center of Geohazards Automatic Monitoring, Ministry of Natural Resources, Chongqing Engineering Research Center of Automatic Monitoring for Geological Hazards, Chongqing 401120, China
    State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China)

  • Hong Xu

    (Technology Innovation Center of Geohazards Automatic Monitoring, Ministry of Natural Resources, Chongqing Engineering Research Center of Automatic Monitoring for Geological Hazards, Chongqing 401120, China)

  • Jie Chen

    (State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China)

  • Yanfei Kang

    (Technology Innovation Center of Geohazards Automatic Monitoring, Ministry of Natural Resources, Chongqing Engineering Research Center of Automatic Monitoring for Geological Hazards, Chongqing 401120, China
    State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China
    School of Civil Engineering, Chongqing University, Chongqing 400044, China)

  • Yuanyuan Pu

    (State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China)

  • Yabo Ye

    (State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China)

  • Jue Tong

    (State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China)

Abstract

Effective landslide hazard prevention requires accurate landslide prediction models, and the data-driven approaches based on deep learning models are gradually becoming a hot research topic. When training deep learning models, it is always preferable to have a large dataset, while most available landslide monitoring data are limited. For data missing or data sparseness problems, conventional interpolation methods based on mathematical knowledge lack mechanism interpretability. This paper proposes that numerical simulations can be used to expand the deep learning dataset we need. Taking the Jiuxianping landslide in the Three Gorges Reservoir Area (TGRA) as the geological background, a finite element numerical model was established, and the landslide displacement time series data were solved considering the boundary conditions of reservoir water level change and precipitation. Next, based on three metrics: Euclidean distance, cosine similarity, and dynamic time warping (DTW) distance, the time series similarity between the displacement data obtained from simulation and data obtained from actual monitoring were verified. Finally, the combined deep learning model was built to predict the displacement of the Jiuxianping landslide. The model was trained on both the simulated and monitored datasets and tested by the last 12 monitored data points. Prediction results with the testing set showed that the models trained using the expanded training set from numerical simulations exhibited lower prediction errors, and the errors had a more concentrated distribution. The results suggest that this landslide displacement prediction method combining numerical simulation and deep learning can solve the problem of inadequate datasets due to low monitoring frequency, as well as provide an interpretation of the physical mechanism for data vacancy filling.

Suggested Citation

  • Wenhan Xu & Hong Xu & Jie Chen & Yanfei Kang & Yuanyuan Pu & Yabo Ye & Jue Tong, 2022. "Combining Numerical Simulation and Deep Learning for Landslide Displacement Prediction: An Attempt to Expand the Deep Learning Dataset," Sustainability, MDPI, vol. 14(11), pages 1-20, June.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:11:p:6908-:d:832338
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    References listed on IDEAS

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    1. Hong Wang & Guangyu Long & Jianxing Liao & Yan Xu & Yan Lv, 2022. "A new hybrid method for establishing point forecasting, interval forecasting, and probabilistic forecasting of landslide displacement," 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. 111(2), pages 1479-1505, March.
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    Cited by:

    1. Meng Zhang & Jiatong Ling & Buyun Tang & Shaohua Dong & Laibin Zhang, 2022. "A Data-Driven Based Method for Pipeline Additional Stress Prediction Subject to Landslide Geohazards," Sustainability, MDPI, vol. 14(19), pages 1-16, September.

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