Online machine learning for stream wastewater influent flow rate prediction under unprecedented emergencies

Accurate influent flow rate prediction is important for operators and managers at wastewater treatment plants (WWTPs), as it is closely related to wastewater characteristics such as biochemical oxygen demand (BOD), total suspend solids (TSS), and pH. Previous studies have been conducted to predict influent flow rate, and it was proved that data-driven models are effective tools. However, most of these studies have focused on batch learning, which is inadequate for wastewater prediction in the era of COVID-19 as the influent pattern changed significantly. Online learning, which has distinct advantages of dealing with stream data, large data set, and changing data pattern, has a potential to address this issue. In this study, the performance of conventional batch learning models Random Forest (RF), K-Nearest Neighbors (KNN), and Multi-Layer Perceptron (MLP), and their respective online learning models Adaptive Random Forest (aRF), Adaptive K-Nearest Neighbors (aKNN), and Adaptive Multi-Layer Perceptron (aMLP), were compared for predicting influent flow rate at two Canadian WWTPs. Online learning models achieved the highest R 2 , the lowest MAPE, and the lowest RMSE compared to conventional batch learning models in all scenarios. The R 2 values on testing data set for 24-h ahead prediction of the aRF, aKNN, and aMLP at Plant A were 0.90, 0.73, and 0.87, respectively; these values at Plant B were 0.75, 0.78, and 0.56, respectively. The proposed online learning models are effective in making reliable predictions under changing data patterns, and they are efficient in dealing with continuous and large influent data streams. They can be used to provide robust decision support for wastewater treatment and management in the changing era of COVID-19 and also under other unprecedented emergencies that could change influent patterns.