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

Wenhan Xu, Hong Xu, Jie Chen, Yanfei Kang, Yuanyuan Pu, Yabo Ye and Jue Tong
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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
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
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

Sustainability, 2022, vol. 14, issue 11, 1-20

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.

Keywords: landslide displacements; deep learning; numerical simulations; time series similarity (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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