Stability Analysis of Recent Failed Red Clay Landslides Influenced by Cracks and Rainfall Based on the XGBoost–PSO–SVR Model

Currently, most studies on slope stability either neglect or consider only one of the two critical factors—rainfall conditions and crack state—that influence the stability of recent landslides. To address this limitation, eleven parameters, including slope height, internal friction angle, cohesion, rainfall conditions, and crack state, were selected as evaluation indexes. GeoStudio software 2018 R2 was also used to simulate the slope safety factor under various parameters, and 363 datasets were obtained. The eXtreme Gradient Boosting–Particle Swarm Optimization–Support Vector Regression (XGBoost–PSO–SVR) model was employed to train the simulation results and construct a predictive model. The MSE of XGBoost–PSO–SVR when compared with the MSE of the single-machine methods of XGBoost and PSO–SVR is reduced by 71.9% and 57.8%, respectively. Furthermore, when compared with four single-machine models—Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), and K-Nearest Neighbors (KNN)—the XGBoost–PSO–SVR model had the smallest MSE of 0.0016 and the largest R2 of 0.9919. Thus, the XGBoost–PSO–SVR model demonstrated superior training performance. The predicted safety factor for a recent landslide in Yongchun County, Fujian Province, China, during 4–7 November 2016 was 0.9658, which closely aligned with the actual conditions. This study could provide a new method for the stability prediction of recent landslides based on various factors, such as rainfall conditions and crack state.