Machine Learning Approaches for Predicting Biochemical Oxygen Demand (BOD₅) Based on Process Parameters in Wastewater Treatment Plants

Abstract

In domestic wastewater treatment plants, wastewater purification is typically carried out through biological processes based on bacteria. For the microorganisms in these systems to survive and function effectively, certain environmental and chemical parameters must be maintained within optimal ranges. These parameters can be monitored at minute, hourly, or daily intervals. However, the laboratory determination of Biochemical Oxygen Demand (BOD), a critical parameter for assessing bacterial activity in advanced biological treatment plants, typically takes 3 to 5 days to obtain results, significantly longer than for other parameters. In this study, the aim was to estimate BOD values using process parameters that can be measured at minute or hourly intervals in a wastewater treatment plant. To develop the predictive model, several Machine Learning algorithms, including Artificial Neural Network, Multiple Linear Regression, Gradient Boosting Regressor, XGBoost, and Random Forest, were employed. The performance of these models was evaluated using the Coefficient of Determination, Root Mean Square Error and Mean Absolute Error metrics. According to the results, the Multiple Linear Regression algorithm achieved the highest predictive performance. The model achieved R² = 0.66 with direct imputation and R² = 0.91 with model-based imputation. These findings demonstrate that machine learning algorithms provide an effective alternative for estimating the BOD parameter, particularly when laboratory analyses require extended processing times.

Keywords

• Machine learning
• Artificial neural network
• Multiple linear regression
• Random forest
• Waste water treatment

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