Prediction of municipal wastewater biochemical oxygen demand using machine learning techniques: A sustainable approach

Abstract

This paper proposes an integrated framework of remote sensing and machine-learning techniques to predict municipal wastewater influent biochemical oxygen demand (BOD5) in wastewater treatment plants (WWTPs). The study compares the performance of several supervised machine-learning algorithms, specifically decision tree, random forest, adaptive boosting, gradient boost, and extreme gradient boosting algorithms, against the wastewater received by two WWTPs in South Kingdom of Bahrain. The gradient boost algorithm model obtained the best results, scoring 1.00 coefficient of determination (R2) and 0.08 mean absolute error (MAE) against Askar WWTP dataset. In addition, the developed model showed its applicability and robustness against Al Dur WWTP dataset scoring 0.95 R2 and 3.93 MAE. This study showed that empirically, using a manual sampling method to obtain developed model input feature readings, the duration of the results can be accelerated by 40 times compared to traditional laboratory procedures. As a result, the duration was reduced from five days to only three hours. On the contrary, using real-time sensors to obtain developed model input readings, BOD5 can be predicted in real-time. The proposed approach mitigates environmental risks and ensures an effective treatment process that meets the effluent quality parameters.