Enhanced Landslide Risk Evaluation in Hydroelectric Reservoir Zones Utilizing an Improved Random Forest Approach

Landslides on reservoir slopes are one of the key geologic hazards that threaten the safe operation of hydropower plants. The aim of our study was to reduce the limitations of the existing methods of landslide risk assessment when dealing with complex nonlinear relationships and the difficulty of quantifying the uncertainty of predictions. We established a multidimensional system of landslide risk assessment that covers geological settings, meteorological conditions, and the ecological environment, and we proposed a model of landslide risk assessment that integrates Bayesian theory and a random forest algorithm. In addition, the model quantifies uncertainty through probability distributions and provides confidence intervals for the prediction results, thus significantly improving the usefulness and reliability of the assessment. In this study, we adopted the Gini index and SHAP (SHapley Additive exPlanations) value, an analytical methodology, to reveal the key factors affecting slope stability and their interaction. The empirical results obtained show that the model effectively identifies the key risk factors and also provides an accurate prediction of landslide risk, thus enhancing scientific and targeted decision making. This study offers strong support for managing landslide risk and providing a more solid guarantee of the safe operation of hydropower station sites.