Vorhersage von Lachgasemissionen bei der Abwasserbehandlung mit Maschinelles Lernen/Deep Learning

The project investigates how machine‑learning and deep‑learning techniques can forecast nitrous‑oxide (N₂O) emissions from wastewater‑treatment plants, comparing mechanistic and data‑driven models on real‑world process data. Advanced feature‑engineering, temporal cross‑validation, and model‑interpretability methods (e.g., SHAP, permutation importance) are applied to evaluate the predictive performance and robustness of algorithms such as XGBoost, Random Forest, k‑NN, and neural networks. The results show that selected ML models can reliably predict N₂O emissions, offering a practical basis for emission‑monitoring soft sensors in treatment facilities.