An Attention-Based CNN-LSTM Method for Effluent Wastewater Quality Prediction

Wastewater treatment is a pivotal step in water resource recycling. Predicting effluent wastewater quality can help wastewater treatment plants (WWTPs) establish efficient operations so as to save resources. We propose CNN-LSTM-Attention (CLATT), an attention-based effluent wastewater quality prediction model, which uses a convolutional neural network (CNN) as an encoder and a long short-term memory network (LSTM) as a decoder. An attention mechanism is used to aggregate the information at adjacent sampling times. A sliding window method is proposed to solve the problem of the prediction performance of the model decreasing with time. We conducted the experiment using data collected from a WWTP in Fujian, China. Our results show that the accuracy of prediction is improved, with MSE decreasing by 0.25, MAPE decreasing by 5% and LER decreasing by 7%, after using the sliding window method. Compared with other methods, CLATT achieves the fastest prediction speed among all the methods based on LSTM and the most accurate prediction performance, with MSE increasing up to 0.92, MAPE up to 0.08 and LER up to 0.11. Furthermore, we performed an ablation study on the proposed method to validate the rationality of the major part of the model, and the results show that the LSTM significantly improves the predictive performance of the model, and the CNN and the attention mechanism also improve the accuracy of the prediction.