Spatiotemporal modeling framework for landslide susceptibility assessment along Clean Energy Transmission Corridors

Against the global backdrop of transitioning to clean energy, China has established the world's largest clean energy power transmission network. However, the stable operation of these clean energy transmission networks is increasingly threatened by landslides under extreme climatic conditions. Given the current lack of clarity regarding the extent of landslide impacts on power transmission lines, it is crucial to systematically assess the potential dynamic spatiotemporal distribution of landslide susceptibility. This study presents the first comprehensive dynamic spatiotemporal prediction of landslide susceptibility for transmission lines in China's loess region, highlighting the urgent need to enhance the resilience of transmission infrastructure in response to escalating extreme climatic events. To address this issue, a boosting ensemble framework was initially employed to construct a preliminary susceptibility model, incorporating comprehensive landslide inventory data and twelve influencing factors. Furthermore, MT-InSAR technology and the K-Means clustering algorithm were utilized to derive long-term surface deformation patterns from 2020 to 2024. Finally, the initial susceptibility assessment was refined by integrating deformation zoning based on slope units, generating the final landslide susceptibility map. The results demonstrate that the Categorical Boosting (CatBoost) model outperformed other methods within the boosting ensemble framework (AUC = 0.914). MT-InSAR analysis revealed a maximum deformation rate of 77 mm/year in the study area, with a cumulative displacement of 373 mm. Time-series deformation clustering further indicated that regions dominated by the second deformation pattern were most prevalent. The enhanced matrix incorporating time-series deformation clusters modified the initial assessment by reclassifying slope units from "very high" susceptibility, resulting in a net reduction from 1,496 units to 394 units—a decrease of 1,102 units. This study refines traditional landslide susceptibility models by incorporating diverse surface deformation trends, thereby addressing the risk overestimation inherent in static models and supporting more precise disaster mitigation along transmission lines.