Analysis Grid Search Optimization of Machine Learning Models for Slope Stability Prediction Supports the Design Construction of Geotechnical Structures and Environmental Development

The accurate prediction of slope stability is crucial for the design and construction of geotechnical structures, as well as for environmental development and risk mitigation. This study explores the application of machine learning (ML) models optimized using the Grid Search method to enhance slope stability predictions. In this study, an in-depth analysis of seven prediction models Logistic Regression (LR), K-Nearest Neighbor (KNN), Support Vector Classifier (SVC), CatBoost, and RUSBoost is provided. These models were evaluated using a grid search approach to find the optimal hyperparameters. The model takes several input features, including pore water ratio (ru), height (H), unit weight (Ƴ), cohesion (c), slope angle (β), and angle of internal friction (ɸ). The output is the slope status, either stable (1) or unstable (0). The generalization ability of classification models is improved by using a 5-fold cross-validation (CV). Evaluation indicators such as AUC, accuracy, and kappa were analyzed, and CatBoost outperformed other machine learning models with the highest AUC of 0.823, accuracy of 0.874, and kappa of 0.642. The results indicate that CatBoost is a highly effective tool for predicting slope stability, surpassing other models in classification accuracy. The capacity and efficiency of RF in deformation prediction models make it the most accurate tool available for forecasting slope stability. Additionally, a comprehensive analysis of feature sensitivity was conducted to determine the most significant characteristics for predicting slope stability. These findings not only enhance geotechnical safety but also contribute to sustainable environmental development by preventing landslides, reducing soil erosion, and supporting responsible land-use planning. The integration of machine learning into slope stability analysis promotes ecological preservation and long-term resilience in infrastructure projects.