CNN-BiLSTM sewage treatment dissolved oxygen concentration prediction model based on attention mechanism

Abstract

Aiming at the characteristics of complex biochemical reaction, nonlinearity and difficult prediction of dissolved oxygen in sewage treatment process, this paper proposes a dissolved oxygen concentration prediction model based on CNN-BiLSTM hybrid artificial neural network. Firstly, the abnormal data is identified and eliminated by data preprocessing, and the missing data is filled by interpolation method. Then, the Pearson correlation coefficient is used to analyze the correlation between dissolved oxygen and other variables. Multiple variable data with good correlation are selected and input into the CNN-BiLSTM network model. The dissolved oxygen concentration is predicted by CNN convolution operation combined with bidirectional long-term and short-term memory neural network (Bi-LSTM), and the time attention mechanism is introduced to learn the weight distribution between different time steps, focusing on the time step that has the greatest impact on dissolved oxygen concentration, so as to improve the prediction accuracy of the model. Compared with LSTM, GRU, CNN-LSTM and CNN-GRU models, the simulation results show that the proposed model can predict the dissolved oxygen more accurately and has higher prediction accuracy.