Spatio-Temporal Deformation Prediction of Large Landslides in the Three Gorges Reservoir Area Based on Time-Series Graph Convolutional Network Model DOI Creative Commons
Juan Ma, Leihua Yao, Lizheng Deng

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(8), P. 4491 - 4491

Published: April 18, 2025

The displacement–time curve of a landslide serves as critical indicator its movement state, with precise deformation prediction being essential for effective disaster early warning. While numerous studies have employed machine learning techniques to predict at individual monitoring points, they often overlook the spatial correlations among points arranged along horizontal and vertical cross-sections. To address this limitation, paper employs Temporal Graph Convolutional Network (T-GCN) model, which integrates strengths Networks (GCNs) Gated Recurrent Units (GRUs). GCN captured while GRU modeled temporal dynamics displacement. T-GCN model was applied spatio-temporal Dawuchang in Three Gorges Reservoir area. Experimental results demonstrated that effectively predicted displacement landslides, offering robust approach warning systems. also incorporated influence external factors, such rainfall reservoir water levels, enhancing accuracy providing valuable insights future research forecasting.

Language: Английский

Spatio-Temporal Deformation Prediction of Large Landslides in the Three Gorges Reservoir Area Based on Time-Series Graph Convolutional Network Model DOI Creative Commons
Juan Ma, Leihua Yao, Lizheng Deng

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(8), P. 4491 - 4491

Published: April 18, 2025

The displacement–time curve of a landslide serves as critical indicator its movement state, with precise deformation prediction being essential for effective disaster early warning. While numerous studies have employed machine learning techniques to predict at individual monitoring points, they often overlook the spatial correlations among points arranged along horizontal and vertical cross-sections. To address this limitation, paper employs Temporal Graph Convolutional Network (T-GCN) model, which integrates strengths Networks (GCNs) Gated Recurrent Units (GRUs). GCN captured while GRU modeled temporal dynamics displacement. T-GCN model was applied spatio-temporal Dawuchang in Three Gorges Reservoir area. Experimental results demonstrated that effectively predicted displacement landslides, offering robust approach warning systems. also incorporated influence external factors, such rainfall reservoir water levels, enhancing accuracy providing valuable insights future research forecasting.

Language: Английский

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