A Machine Learning Approach to Predicting the Turbidity from Filters in a Water Treatment Plant

Rapid sand filtration is a critical step in the water treatment process, as its effectiveness directly impacts the supply of safe drinking water. However, optimising filtration processes in water treatment plants (WTPs) presents a significant challenge due to the varying operational parameters and conditions. This study applies explainable machine learning to enhance insights into predicting direct filtration operations at the Ålesund WTP in Norway. Three baseline models (Multiple Linear Regression, Support Vector Regression, and K-Nearest Neighbour (KNN)) and three ensemble models (Random Forest (RF), Extra Trees (ET), and XGBoost) were optimised using the GridSearchCV algorithm and implemented on seven filter units to predict their filtered water turbidity. The results indicate that ML models can reliably predict filtered water turbidity in WTPs, with Extra Trees models achieving the highest predictive performance (R2 = 0.92). ET, RF, and KNN ranked as the three top-performing models using Alternative Technique for Order of Preference by Similarity to Ideal Solution (A-TOPSIS) ranking for the suite of algorithms used. The feature importance analysis ranked the filter runtime, flow rate, and bed level. SHAP interpretation of the best model provided actionable insights, revealing how operational adjustments during the ripening stage can help mitigate filter breakthroughs. These findings offer valuable guidance for plant operators and highlight the benefits of explainable machine learning in water quality management.