Author

Adrienne Breef-Pilz

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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