Deep learning based data-driven model for detecting time-delay water quality indicators of wastewater treatment plant influent

Rapid and accurate detection of time-delayed water quality indicators (WQIs) is the key to achieving fast feedback regulation of wastewater treatment plants (WWTPs) that enables its energy-efficient operation and high tolerance towards shock sewage loads. However, advanced oxidation methods are costly, and data-driven modeling methods based on traditional machine learning algorithms for detecting time-delayed WQIs have limited detection accuracy. This work develops deep learning models based on long short-term memory (LSTM) neural networks to detect time-delayed WQIs in WWTPs intake accurately. The lack of interpretability of the deep learning models hampers the optimization of the developed LSTM models in applications. Therefore, a global sensitivity analysis (GSA) based on Shapley additive explanations (SHAP) is performed to quantify the contribution of the input indicators to detection results of the developed LSTM models. The direct contributions provide the basis for optimizing the input indicators to achieve more cost-effective modeling detection. In the case study, the developed LSTM models achieved good accuracy (R2 of 0.9141, 0.9239, and 0.9040, respectively) in detecting chemical oxygen demand, total nitrogen, and total phosphorus in the influent of a WWTP, outperforming the four types of baseline models. According to the SHAP values, the contributions of dissolved oxygen, turbidity, and ammonia nitrogen to the above detection targets are always in the top third of all input indicators, which are more outstanding than meteorological indicators. Removing the indicator with the smallest SHAP value reduces the build and run costs of the models with minimal loss of detection accuracy. Combining deep learning and GSA to detect WWTPs influent is a novel and effective attempt. This attempt provides a more sustainable solution for rapid and accurate detection of time-delayed WQIs, which drives WWTPs' operation in an intelligent, clean, and safe direction.