A novel total nitrogen prediction method based on recurrent neural networks utilizing cross-coupling attention and selective attention

Abstract

Considering the critical role the total nitrogen (TN) plays in the stable operation of wastewater treatment plants (WWTPs), it is necessary to predict its future variation accurately. However, limited by current detection techniques, only a few kinds of water quality parameters (WQPs) with finite historical data can be obtained in the WWTPs. The small sample size of WQPs tremendously hinders the precision of TN prediction. In this study, a novel cross-coupling attention recurrent neural network (CCA-RNN) is proposed to overcome this problem. First, cross-coupling attention (CCA) is designed to enable the topological structures in the historical input data to be directly and effectively extracted with fewer training samples. Then, selective attention is introduced to dynamically select the useful topological relationships and the corresponding variables sent by the high-speed channel. Compared with the other state-of-the-art methods, CCA-RNN achieves the best performance on both the public and practical wastewater datasets, proving its superiority and great potential to be deployed in similar problems.