Development of local and global wastewater biochemical oxygen demand real-time prediction models using supervised machine learning algorithms

This study aims to shed light on a new understanding of global machine-learning (ML) prediction models for wastewater treatment plants (WWTPs). The paper evaluates the development of local and global prediction models to predict the wastewater influent biochemical oxygen demand (BOD5) in four WWTPs. The paper proposes an integrated framework of remote sensing and ML techniques, specifically decision tree, random forest, adaptive boosting, and gradient boost algorithms. The modeling considered two cases for local and global models. The local model's best score achieved 0.92 coefficient of determination (R2), 6.56 mean absolute error (MAE), and 1.00 R2, 0.08 MAE, respectively. In the second case, the global model's best score achieved 0.58 R2, 20.73 MAE, and 0.95 R2, 3.93 MAE, respectively. The results showed that the developed local model reduced the BOD5 test results duration from five days to only three hours. However, in the first case, the developed global model failed to achieve good predictions against other WWTPs. This is due to the biological factors that change wastewater characteristics from one place to another. The study concluded that a local model is recommended to be developed for each WWTP separately. The novelty of this paper is that it investigates the various cases of testing the performance of the ML prediction models against different WWTPs than the one used to train and test. Also, it technically discusses the biological factors of wastewater that result in complications in ML modeling and prediction, which was never discussed or taken into consideration in ML literature.