Reservoir Basin-Scale Landslide Susceptibility Assessment by Machine Learning Techniques: A Case Study of San Pietro Dam, Southern Italy

Research on landslides around reservoirs is necessitated to strengthen risk prevention and mitigation, as their occurrence has catastrophic consequences. For reservoir safety assessments, landslide susceptibility analysis is commonly concentrated on single reservoir bank slopes or individual landslides. However, focusing solely on bank slopes and individual landslides gives an incomplete picture of how safe the reservoir is from possible landslide related risks, since landslides from distant slopes can also adversely affect the reservoir in different ways. In this paper, landslide susceptibility assessment was conducted using machine learning models (Gradient Boosting Machine, XGBoost, Random Forest and Ensemble Stacking) in the area around the San Pietro Dam, an earth dam located in Southern Italy, in a region highly prone to landslide hazards. The landslide inventory for the area was used to generate landslide and non-landslide points for model training and testing. The area under curve (AUC) of a receiver operating characteristic (ROC) curve approach was used to evaluate, validate, and compare the performance of the four models. Results indicated that the ROC AUC values of the models ranged from 0.76 to 0.77, with the Random Forest, Gradient Boosting and Ensemble stacking models having AUC values of 0.77. All the models classified about 15–20% of the reservoir basin as highly susceptible to landslides. The generated basin-scale landslide susceptibility maps can be used to prioritize monitoring and maintenance in areas around the dam that have been identified as highly susceptible.