Making Sense of Messy Environmental Data

Environmental science often deals with messy data. Small, varied data sets make it hard to build reliable models. Traditional machine learning methods often fail here. They fit the training data too well but perform poorly in real-world tests. Enter GP-BT. It's a new method designed to tackle these issues. It uses Gaussian Processes and Bayesian Tuning. The goal? To find the best model that generalizes well, not just fits the training data. GP-BT does this by minimizing cross-validation loss. It's a smart way to avoid overfitting. Tests were done on three environmental data sets. GP-BT outperformed other methods like Random Forest, XGBoost, and CatBoost. It also did better than standard Gaussian Process models. The proof? 52 lab experiments showed lower prediction errors for unseen conditions.In one case, GP-BT was used to optimize sewer overflow treatment. It predicted a 98% removal efficiency. That's way better than the 89% predicted by an overfitted Random Forest model. Lab tests confirmed these results. This shows how traditional methods might miss important insights. GP-BT also provides better explanations. SHapley Additive exPlanations (SHAP) analysis showed that GP-BT's interpretations aligned better with known science. It focused on important factors like reagent use, not just random variables. This method is a big deal. It helps researchers get reliable insights from small data sets. It's a practical tool for environmental science. To make it accessible, an open-source Python package and a web platform are available.