When excavating the rock foundation of a hydropower station, it will be affected by the phenomenon of unloading and relaxation, which may increase the risk of stability of the dam foundatio...

Hydrodynamically triggered landslides remain a major concern in reservoir regions, where the mechanisms controlling displacement evolution are still not fully understood and the multi-scale deformation responses induced by individual hydrodynamic factors remain difficult to quantify. To address these issues, this study establishes a TSD-TET composite framework by integrating time-series signal decomposition with deep learning for multi-scale displacement prediction and the mechanism-oriented interpretation of hydrodynamically triggered landslides. The monitored displacement sequence is first decomposed into physically interpretable components, including trend, periodic, and random terms. Each component is subsequently predicted using deep temporal learning models to capture different deformation characteristics at multiple temporal scales. Meanwhile, key hydrodynamic driving factors, including rainfall, reservoir water level, and groundwater level, are decomposed within the same framework to examine their statistical associations with different displacement components. The proposed approach is applied to the Donglingxin landslide located in the Sanbanxi Hydropower Station reservoir area. Results show that the model achieves high prediction accuracy under both long-term forecasting horizons and limited-sample conditions, with a cumulative displacement coefficient of determination reaching R² = 0.945. Mechanism analysis further indicates that trend deformation is mainly controlled by geological structure and gravitational loading, periodic deformation is strongly modulated by hydrological cycles associated with reservoir water level fluctuations, and random deformation is more likely to reflect short-term disturbances and transient hydrodynamic forcing. These findings provide new insights into the deformation mechanisms of hydrodynamically triggered landslides and offer a promising technical pathway for improving displacement prediction, monitoring, and early warning of reservoir-induced landslide hazards.

Landslide-generated surge waves are significant natural hazards, posing severe risks to engineering safety. Despite extensive research on the dynamics of landslide-generated waves, studies analyzing controlling factors and their mechanisms remain limited, leaving key influencing processes inadequately understood. This study utilizes computational fluid dynamics (CFD) to perform a numerical simulation of a semi-submerged landslide in a hydropower station reservoir area. The research systematically investigated the effects of key variables, including slide volume, velocity, centroid height, and water depth, on the behavior of semi-submerged landslide-generated surge waves. Results demonstrate a positive correlation of slide volume, velocity, and centroid height with the initial wave height and run-up on the opposing shoreline. However, the impact of water depth reveals a more complex pattern, exhibiting distinct surge characteristics in the near-field and far-field zones. Via correlation and sensitivity analyses, this study elucidated the relationships between these factors and surge dynamics, identifying the primary factors influencing the size of the semi-submerged landslide-generated surge. The findings provide critical insights for predicting and mitigating surge disasters, offering both theoretical foundations and practical application value for landslide disaster prevention and management.