Interpretable and Calibrated XGBoost Framework for Risk-Informed Probabilistic Prediction of Slope Stability
Year: 2025
Type: article
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 (ru), 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 (Pf) decreases as c and φ increase and increases with β, H, and ru. 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 Pf-feature curves. SHAP analysis validated physically consistent influence rankings (φ > H ≈ c > β > γ > ru). Predicted probabilities were classified into Low (Pf < 0.01), Medium (0.01 ≤ Pf ≤ 0.10), and High (Pf > 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.
Source: Sustainability
DOI: 10.3390/su172210122
Author: Hani S. Alharbi
Institution: Shaqra University
Language: English
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Funder: Shaqra University
OpenAlex ID: W4416165055