3D displacement time series prediction of a north-facing reservoir landslide powered by InSAR and machine learning

Abstract

Active landslides pose a significant threat globally, endangering lives and property. Effective monitoring and forecasting of displacements are essential for the timely warnings and mitigation of these events. Interferometric synthetic aperture radar (InSAR) stands out as an efficient and prevalent tool for monitoring landslide deformation and offers new prospects for displacement prediction. However, challenges such as inherent limitation of satellite viewing geometry, long revisit cycles, and limited data volume hinder its application in displacement forecasting, notably for landslides with near-north-south deformation less detectable by InSAR. To address these issues, we propose a novel strategy for predicting three-dimensional (3D) landslide displacement, integrating InSAR and global navigation satellite system (GNSS) measurements with machine learning (ML). This framework first synergizes InSAR line-of-sight (LOS) results with GNSS horizontal data to reconstruct 3D displacement time series. It then employs ML models to capture complex nonlinear relationships between external triggers, landslide evolutionary states, and 3D displacements, thus enabling accurate future deformation predictions. Utilizing four advanced ML algorithms, i.e. random forest (RF), support vector machine (SVM), long short-term memory (LSTM), and gated recurrent unit (GRU), with Bayesian optimization (BO) for hyperparameter tuning, we applied this innovative approach to the north-facing, slow-moving Xinpu landslide in the Three Gorges Reservoir Area (TGRA) of China. Leveraging over 6.5 years of Sentinel-1 satellite data and GNSS measurements, our framework demonstrates satisfactory and robust prediction performance, with an average root mean square deviation (RMSD) of 9.62 mm and a correlation coefficient (CC) of 0.996. This study presents a promising strategy for 3D displacement prediction, illustrating the efficacy of integrating InSAR monitoring with ML forecasting in enhancing landslide early warning capabilities.