Slope Stability Prediction Based on CCO-Optimized XGBoost Model

Achieving rapid and accurate prediction of slope stability is a key scientific issue in slope engineering. However, slope stability is governed by multi-factor coupling effects and exhibits strong nonlinear characteristics, and traditional slope stability prediction methods have clear limitations in predicting the stability of complex slope engineering. To address such problems, a CCO-optimized XGBoost model for slope stability prediction is constructed by using the Cuckoo Catfish Optimizer (CCO) to optimize the hyperparameters of XGBoost, thereby enhancing prediction accuracy and robustness. Based on 832 slope cases covering various slope engineering scenarios, six key feature parameters—slope height (H), slope angle (β), unit weight (γ), cohesion (C), internal friction angle (φ), and pore pressure ratio (ru)—were selected to construct the dataset. Ten-fold cross-validation was adopted for model training and robustness testing. After optimization, the optimized model achieved an accuracy of 0.898, precision of 0.892, recall of 0.902, F1-score of 0.897, and AUC of 0.944, representing its optimal comprehensive performance. These evaluation metrics are significantly better than those of unoptimized XGBoost, RandomForest, LightGBM, and the representative PSO-XGBoost model. The SHAP analysis method was used to improve the interpretability of model predictions. Prediction analysis was carried out using 12 sets of real engineering cases, and the prediction results were consistent with the actual conditions, further verifying the model’s generalization ability. This model shows favorable performance in slope stability prediction and can provide a reference method for slope stability evaluation, decision optimization and risk prevention and control in slope engineering.