A Robust Gaussian Process Paradigm for Predictive Modeling on Small Data sets in Environmental Science: A Case Study in Ballasted Flocculation

Environmental processes including ballasted flocculation (BF) present significant optimization challenges due to complex multicomponent interactions and small, heterogeneous experimental data sets that frequently lead to overfitted machine learning (ML) models with poor real-world performance. To address this, we developed GP-BT, a Gaussian Process Bayesian Tuning framework that systematically optimizes kernel selection and hyperparameters by directly minimizing cross-validation loss, explicitly prioritizing generalization over training set fitting. Comprehensive evaluation across three environmental data sets demonstrated GP-BT's superior robustness compared to conventional algorithms (Random Forest, XGBoost, CatBoost) and standard GP models. The GP-BT's practical value was confirmed through 52 independent laboratory experiments, achieving lower prediction errors on unseen conditions. The method's conservative learning strategy─avoiding aggressive fitting of sparse, noisy data points─proved crucial for reliable real-world performance. Applied to combined sewer overflows treatment optimization, GP-BT uncovered experimental conditions achieving 98% removal efficiency, compared to 89% predicted by the overfitted Random Forest model. Experimental validation confirmed these predictions, revealing substantial process potential masked by traditional modeling approaches. SHapley Additive exPlanations (SHAP) analysis showed that GP-BT's interpretations better aligned with established physicochemical mechanisms, properly emphasizing reagent importance over less controllable factors. Beyond specific applications, this work provides environmental researchers with a ready-to-use, rigorously validated framework for extracting reliable insights from costly, small-scale experimental data sets. To maximize impact, we provide an open-source Python package (pip install bayesian-gp-cvloss) and interactive web platform (www.ai4env.world), enabling widespread adoption of robust ML practices that can accelerate discovery of hidden performance potential in environmental technologies.