Machine learning-driven integration of time-series InSAR and multiple surface factors for landslide identification and susceptibility assessment

Landslides pose a significant threat to the safety of reservoirs, particularly those situated in canyon terrains. This study aims to enhance the safety and security of reservoir areas by proposing an integrated method for the automatic identification and assessment of landslides. By combining the SBAS-InSAR technique with spatial clustering analysis, we successfully delineated landslide areas and developed a new landslide susceptibility assessment model. This model operates independently of historical landslide inventory data. Based on the delineated landslide areas, we enhanced the information value model using the inverse tangent function, which was then integrated with Random Forest and Extreme Gradient Boosting methods for landslide susceptibility assessment. The identified landslides were validated through field tests, demonstrating a high degree of consistency with actual conditions. The results indicated that, in canyon-type reservoirs, aspect was a critical factor influencing landslide occurrence, with susceptibility being greater near water bodies. In model comparisons, the RF-NIV model outperformed, providing a more realistic representation of landslide susceptibility distribution. These findings offer valuable insights for landslide safety management in canyon-type reservoirs, such as those in Hekou Village and Baihetan.