Landslide Prediction for Slopes Considering The Influence of Environmental and Geotechnical Factors Through Machine Learning

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Abstract
Objectives: This study aims to predict slope stability using machine learning algorithms, considering geotechnical and geometric variables to improve the accuracy of safety factor estimation and mitigate risks associated with slope failures. Theoretical Background: Traditional limit equilibrium methods, such as Bishop's method, present limitations when dealing with complex environmental conditions. Recent literature has highlighted the potential of machine learning techniques, such as Random Forest, Neural Networks, and XGBoost, in modeling nonlinear and highly variable systems, which are typical in geotechnical problems. Method: A parametric dataset with 1,520 observations generated through GeoSlope simulations was used, containing variables such as cohesion, internal friction angle, height, slope angle, and water level. Four algorithms were implemented: Random Forest, Gradient Boosting, Artificial Neural Networks, and XGBoost. Model performance was evaluated through cross-validation, hyperparameter tuning, and metrics including R², MSE, and RMSE. Results and Discussion: The XGBoost model achieved the best performance (R² = 0.9841; RMSE = 0.1419), followed by Gradient Boosting and Neural Networks. Variable importance analysis indicated that cohesion and internal friction angle are the primary factors influencing slope stability. Research Implications: The findings reinforce the applicability of machine learning models as decision-support tools in geotechnical engineering projects, offering greater precision and efficiency compared to traditional methods. Originality/Value: This study advances the field by integrating multiple modern algorithms within a replicable modeling framework, demonstrating superior performance over previous approaches and expanding the scope for practical applications in geotechnical engineering.