Random forest-based prediction of shallow slope stability considering spatiotemporal variations in unsaturated soil moisture.

ABSTRACT: With the increasing incidence of extreme rainfall driven by global climate change, geological hazards like landslides have become more prevalent. This study proposed an efficient framework that combined machine learning and physical models to enhance computational efficiency and reliability for regional slope stability predictions under extreme rainfall. The GEOtop model was employed to simulate volumetric water content (VWC) in unsaturated soil of an area in Singapore under maximum daily and maximum 5-day antecedent rainfall conditions. The result of analyses was then incorporated into Scoops3D for factor of safety (FOS) calculations. The random forest (RF) models were trained using VWC under maximum daily rainfall and applied to predict slope stability under maximum 5-day antecedent rain

SUBMITTER: Li Y 

PROVIDER: S-EPMC11906632 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature