Interpretable and Calibrated XGBoost Framework for Risk-Informed Probabilistic Prediction of Slope Stability

Hani S. Alharbi ()
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Hani S. Alharbi: Civil Engineering Department, College of Engineering, Shaqra University, Dawadmi, Riyadh 11911, Saudi Arabia

Sustainability, 2025, vol. 17, issue 22, 1-22

Abstract: This study develops an interpretable and calibrated XGBoost framework for probabilistic slope stability assessment using a 627-case database of circular-mode failures. Six predictors, namely, unit weight (γ), cohesion (c), friction angle (φ), slope angle (β), slope height (H), and pore-pressure ratio (r u ), were used to train a gradient-boosted tree model optimized through Bayesian hyperparameter search with five-fold stratified cross-validation. Physically based monotone constraints ensured that failure probability (P f ) decreases as c and φ increase and increases with β, H, and r u . The final model achieved strong performance (AUC = 0.88, Accuracy = 0.80, MCC = 0.61) and reliable calibration, confirmed by a Brier score of 0.14 and ECE/MCE of 0.10/0.19. A 1000-iteration bootstrap quantified both epistemic and aleatoric uncertainties, providing 95% confidence bands for P f -feature curves. SHAP analysis validated physically consistent influence rankings (φ > H ≈ c > β > γ > r u ). Predicted probabilities were classified into Low (P f < 0.01), Medium (0.01 ≤ P f ≤ 0.10), and High (P f > 0.10) risk levels according to geotechnical reliability practices. The proposed framework integrates calibration, uncertainty quantification, and interpretability into a comprehensive, auditable workflow, supporting transparent and risk-informed slope management for infrastructure, mining, and renewable energy projects.

Keywords: slope stability; probabilistic modeling; XGBoost; SHAP interpretability; geotechnical risk; sustainable infrastructure (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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