Machine Learning-Based Prediction of Settled Water Turbidity for Optimizing Alum Dosage in Drinking Water Treatment
Abstract
Efficient chemical dosing is essential in drinking water treatment plants to ensure effective turbidity removal while minimizing chemical consumption and operational costs. Conventional alum dosing methods rely on manual jar tests and operator experience, which are time-consuming and often inadequate for handling rapid variations in raw water quality. Key parameters such as turbidity, pH, and electrical conductivity significantly influence the coagulation process, making accurate dosage determination a complex and dynamic challenge. This study proposes a machine learning–based approach to support optimal alum dosage selection by predicting settled water turbidity using historical treatment data. The dataset includes raw water turbidity, pH, conductivity, and applied alum dosage, which are used to train regression models capable of capturing nonlinear relationships between input parameters and treatment performance. The study used 7,975 hourly operational records collected from Ambathale Water Treatment Plant during January–December 2024, and model evaluation was performed using a time-aware 80:20 chronological train-test split. Multiple algorithms, including Gradient Boosting, Random Forest, XGBoost, and LightGBM, were explored alongside feature engineering techniques to improve model effectiveness. Experimental results demonstrate that the Random Forest model achieves superior performance, with an R² score of 0.7909, RMSE of 0.5197, and MAE of 0.3166, indicating strong…
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