Applications of Tree Based Regressors in Predicting Factor of Safety in Slope Stability and Observing Feature Importance

In the geotechnical engineering field, it is required to anticipate the Factor of Safety (FOS) in slope stability precisely in order to assess the possibility of slope failure and guarantee infrastructure safety. This research utilizes a thorough slope stability dataset to inspect how well six tree-based regression models—Decision Tree, Random Forest, Extra Trees, AdaBoost, Gradient Boosting, and XGBoost—predict the FOS. With the target of predicting the continuous FOS value, the dataset covers 10,000 samples with eight vital geotechnical parameters and one categorical reinforcement feature. Using performance metrics like RMSE, MAE, R2 score, and execution time, a modified study was executed. The most significant factors affecting slope stability were also resolved using feature importance analysis. The Extra Trees Regressor performs finer than other models in terms of predictive accuracy, according to the results, while cohesion, internal friction angle, slope angle, and pore water pressure ratio decrease.