Development of machine learning surrogate models for slope stability prediction using AI techniques: a case study of the Meizhou landslide.

ABSTRACT: Rainfall-induced landslides present a critical global geohazard, necessitating the development of robust, rapid tools for slope stability evaluation. This study proposes a hybrid framework that integrates numerical modeling with machine learning (ML) regression to predict the Factor of Safety (FoS) under dynamic groundwater conditions. Using the geometry of the recent Meizhou landslide in China as a baseline, a parametric study was conducted via GeoStudio's limit equilibrium analyses to generate a dataset of 249 simulations based on five key geotechnical parameters: cohesion, friction angle, unit weight, surcharge load, and groundwater level. Three regression-based ML models such as Random Forest (RF), Ordinary Least Squares (OLS), and Extreme Gradient Boosting (XGBoost) were trained to de

SUBMITTER: Khan MI 

PROVIDER: S-EPMC12834960 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature