Intelligent Prediction Based on NRBO–LightGBM Model of Reservoir Slope Deformation and Interpretability Analysis

Predicting slope deformation is pivotal for reservoir safety management; however, quantitative attribution to hydrologic–temporal factors with interpretable and hyperparameter-robust models under multi-point temporal dependence is still rare. Hence, we develop an interpretable hybrid framework that couples a Light Gradient Boosting Machine (LightGBM) with a Newton–Raphson-based optimizer (NRBO) for hyperparameter tuning. Unsupervised clustering is first employed to capture intrinsic temporal associations among multiple monitoring points. Subsequently, the NRBO–LightGBM framework is proposed to enhance prediction accuracy and model robustness in slope deformation prediction. Finally, SHAP analysis is integrated to quantify the contribution of influencing factors, thereby strengthening the physical interpretability and credibility of the model. The proposed framework is validated using long-term deformation monitoring data from the Lijiaxia Hydropower Station. Comparative experiments indicate that the NRBO–LightGBM model achieves a 22.8% reduction in RMSE and an 11.4% increase in R2 relative to conventional statistical models, improving prediction accuracy with a 21.5% lower RMSE and a 15.5% higher R2 compared with the baseline LightGBM. Furthermore, SHAP interpretability analysis elucidates the internal predictive mechanism, revealing that deformation evolution is primarily governed by temporal accumulation and seasonal variations represented by the time variable t and periodic components. Overall, the NRBO–LightGBM model provides high-precision and interpretable deformation prediction for reservoir slopes, effectively bridging predictive performance with mechanistic understanding and offering actionable insights for landslide early warning and risk management.