Landslide Displacement Prediction With Gated Recurrent Unit and Spatial-Temporal Correlation

Landslides are geohazards of major concern that can cause casualties and property damage. Short-term landslide displacement prediction is one of the most critical and challenging tasks in landslide deformation analysis, and is beneficial for future hazard mitigation. In this research, a novel short-term displacement prediction approach using spatial-temporal correlation and a gated recurrent unit (GRU) is proposed. The proposed approach is a unified framework that integrates time-series instant displacements collected from multiple monitoring points on a failing slope. First, a spatial-temporal correlation matrix, including the pairwise Pearson's correlation coefficients, was studied based on the temporal instant displacement data. Then, the extracted spatial features were integrated into the time-series prediction model using GRU. This approach combines both spatial and temporal features simultaneously and provides enhanced prediction performance. In the last step, a comparative analysis against other benchmark algorithms is performed in two case studies including the conventional time-series modeling approach and the spatial-temporal modeling approach. The computational results show that the proposed model performs best in terms of performance evaluation metrics.

Automated summary

In this research, a novel short-term displacement prediction approach using spatial-temporal correlation and a gated recurrent unit (GRU) is proposed, which integrates time-series instant displacements collected from multiple monitoring points and provides enhanced prediction performance.