Open access
Sep 2026
Advancing Near-Term Water Quality Forecasting with Explainable Machine Learning and Probabilistic Uncertainty Quantification
An end-to-end framework integrating an extreme gradient boosting (XGBoost) ML with automated hyperparameter optimization, bootstrap-ensemble uncertainty quantification, and interpretability through SHapley additive exPlanations (SHAP), which was applied to forecast phytoplankton in a water-supply reservoir.
R. Shukla, Cayelan C. Carey, Adrienne Breef-Pilz et al.
· ACS ES&T Water · 0 citations