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

Abstract

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.

Machine Learning-Based Slope Failure Prediction Model Considering the Uncertainty of Prediction

“…The major disadvantage of physicsbased models is that obtaining an exact mathematical formula is often oversimplified and impractical, especially in complex geological settings [5]. These limitations lead to the formation of models that are not sufficiently precise or efficient for predicting the slope failure [6].…”