Spatiotemporal Prediction of Landslide Displacement Using Graph Convolutional Network-Based Models: A Case Study of the Tangjiao 1# Landslide in Chongqing, China

Abstract

Landslide displacement monitoring can directly reflect the deformation process of a landslide. Predicting landslide displacements using monitored time series data through deep learning is a useful method for landslide early warning. Currently, existing prediction models mainly focus on single-point time series displacement prediction and do not consider the spatial relationship between monitoring points. To fully take into account the temporal and spatial correlation of displacement monitoring data, this paper proposes two models based on the graph convolutional network (GCN) to perform spatiotemporal prediction of the displacement of the Tangjiao 1# landslide. Firstly, the landslide monitoring system is transformed into a fully connected graph (FCG) to depict the spatial relationship among monitoring points on the landslide. Secondly, a temporal graph convolutional network (T-GCN) model and an attention temporal graph convolutional network (A3T-GCN) model of landslide displacement based on the GCN and GRU models are established respectively. Thirdly, the two models are used to predict the displacement of the Tangjiao 1# landslide. The results show that the established spatiotemporal prediction models are effective in predicting the displacement of the Tangjiao 1# landslide, and the proposed A3T-GCN model achieves the highest prediction accuracy. Our conclusion validates the effectiveness of the attention mechanism in predicting landslide displacement.

Keywords

• landslide displacement prediction
• deep learning
• graph convolutional network (GCN)
• attention mechanism