Research on BiLSTM-GRU Water Quality Prediction Model Based on Attention Mechanism

Water quality prediction is an important part of water resource monitoring and control. To solve the problem of poor performance of traditional neural networks in the field of water quality prediction. This article introduces the BiLSTM-GRU structure and Attention mechanism to jointly form a prediction model and uses water quality data from a sewage treatment plant in Shanghai as a sample. Experimental analysis shows that the BiLSTM-GRU hybrid model has higher accuracy compared to single BiLSTM model and single GRU model, under the Attention mechanism. This model provides a new reference for predicting water quality in sewage treatment processes.