Advanced Prediction of Landslide Deformation Through Temporal Fusion Transformer and Multivariate Time-Series Clustering of InSAR: Insights From the Badui Region, Eastern Tibet

This study focuses on the Badui region in eastern Tibet, an area with complex topography featuring numerous valleys, ravines, and frequent geological hazards. Given the economic expansion in this region, advanced techniques are essential for analyzing the distribution of geological hazards and developing early warnings of geological hazards. The research employs enhanced small baseline subset interferometric synthetic aperture radar (ESBAS-InSAR) technology, which provides more ascending and descending data than traditional small baseline subset-InSAR (SBAS-InSAR), allowing reprojection into vertical and horizontal components. Following dimensionality reduction through principal component analysis (PCA) and k-means clustering, the horizontal displacements were categorized into four clusters, and the vertical displacements were categorized into five clusters. Time-series data of vertical and horizontal displacements, rainfall, and normalized difference vegetation index (NDVI) were then used to assess 16 displacement prediction models. The temporal fusion transformer (TFT) model demonstrated the best predictive performance. To further improve accuracy, 11 static variables such as clusters, elevation, slope, aspect, distance from faults, time-varying known categorical variable, and earthquake times, were added as the TFT input variables. Results indicate that the optimized TFT model reduces the root-mean-square error (RMSE) from 3.4842 to 2.1707, the mean absolute percentage error (MAPE) from 2625.6399 to 2154.5505, and the mean absolute error (MAE) from 2.4392 to 2.3731. Overall, this study provides a framework for multivariate, multistep forecasting of diverse deformation modes across large areas and identifies distinct landslide deformation patterns through clustering, thereby enhancing the prediction of landslide deformation.