The accurate prediction of landslide susceptibility relies on effectively handling landslide absence samples in machine learning (ML) models. However, existing research tends to generate these samples in feature space, posing challenges in field validation, or using physics-informed models, thereby limiting their applicability. The rapid progress of interferometric synthetic aperture radar (InSAR) technology may bridge this gap by offering satellite images with extensive area coverage and precise surface deformation measurements at millimeter scales. Here, we propose an InSAR-based sampling strategy to generate absence samples for landslide susceptibility mapping in the Badong–Zigui area near the Three Gorges Reservoir, China. We achieve this by employing a Small Baseline Subset (SBAS) InSAR to generate the annual average ground deformation. Subsequently, we select absence samples from slopes with very slow deformation. Logistic regression, support vector machine, and random forest models demonstrate improvement when using InSAR-based absence samples, indicating enhanced accuracy in reflecting non-landslide conditions. Furthermore, we compare different integration methods to integrate InSAR into ML models, including absence sampling, joint training, overlay weights, and their combination, finding that utilizing all three methods simultaneously optimally improves landslide susceptibility models.

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 warning of geological hazards. The research employs ESBAS-InSAR (Enhanced Small Baseline Subset Interferometric Synthetic Aperture Radar) technology, which provides more ascending and descending data than traditional SBAS-InSAR, allowing reprojection into vertical and horizontal components. Following dimensionality reduction through PCA (Principal Component Analysis) 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 NDVI (Normalized Difference Vegetation Index) 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, and time varying known categorical variable, earthquake times, were added as the TFT input variables. Results indicate that optimized TFT model reduces the RMSE (Root Mean Square Error) from 3.4842 to 2.1707, the MAPE (Mean Absolute Percentage Error) from 2625.6399 to 2154.5505, and the MAE (Mean Absolute Error) 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.

Disasters caused by landslides pose a considerable threat to people’s lives and property, resulting in substantial losses each year. Landslide displacement rate prediction (LDRP) provides a useful fundamental tool for mitigating landslide disasters. However, more accurately predicting LDRP remains a challenge in the study of landslides. Lately, ensemble deep learning algorithms have shown promise in delivering a more precise and effective spatial modeling solution. The core aims of this research are to explore and evaluate the prediction capability of three progressive evolutionary deep learning (DL) techniques, i.e., a recurrent neural network (RNN), long short-term memory (LSTM), and a gated recurrent unit (GRU) ensemble AdaBoost algorithm for modeling rainfall-induced and reservoir-induced landslides in the Baihetan reservoir area in China. The outcomes show that the ensemble DL model could predict the Wangjiashan landslide in the Baihetan reservoir area with improved accuracy. The highest accuracy was achieved in the testing set when the window length equaled 30. However, assembling two predictors outperformed the accuracy of assembling three predictors, with the mean absolute error and root mean square error reaching 1.019 and 1.300, respectively. These findings suggest that the combination of strong learners and DL can yield satisfactory prediction results.