Straightforward slope stability prediction under seismic conditions using machine learning algorithms

Abstract One of the most significant and crucial issues in geotechnical engineering works, such as earth dams, embankments, and landfills to name a few, is slope stability assessment. Better methods are required to anticipate slope collapse because of its fatal effects. The goal of this research is to create a straightforward machine learning (ML) model for examining slope stability under seismic conditions. Four ML algorithms are examined, including Logistic Regression (LR), Quadratic Discriminant Analysis (QDA), Light Gradient Boosting Machine (LGBM), and Linear Discriminant Analysis (LDA). The models are trained and tested on the database containing 700 slopes. 10-fold cross validation is utilized for parameter tuning, model training and performance estimating of machine learning models using training set. The best model is interpreted using the SHapley Additive exPlanations (SHAP) method, which is built on game theories. Among the studied models, the LGBM model is the most accurate model based on ranking technique. Most influential features for slope stability prediction under seismic conditions are detected by the SHAP method as follows: peak ground acceleration, friction angle, and angle of inclination.