River Dissolved Oxygen Prediction Based on DA-CNN-BiLSTM
Dissolved oxygen is an important index to measure water quality.The accurate prediction of dissolved oxygen can provide reference for scientific management and protection of water environment.Considering that dissolved oxygen was affected by many external factors and the data had strong nonlinear and non-stationary characteristics,the DA-CNN-BiLSTM dissolved oxygen prediction model was proposed.The CNN layer was used to extract local features of data.Spatial attention focused on features that had a higher impact on prediction outcomes.BiLSTM mined the attribute relationships of the input sequence.Temporal attention layer captured the time dependence of different moments.The model was applied to the dissolved oxygen prediction of three water quality monitoring stations in Minjiang River,Fujian Province.By comparing with the baseline model,it shows that the DA-CNN-BiLSTM model has a better prediction of DO concentration compared with the baseline model.The predicted value of the model is closer to the measured value and the DO prediction performance is optimal.The predic-tion performance of the model has been improved after adding the spatial attention mechanism.
attention mechanismCNN-BiLSTM modeltime series predictiondissolved oxygen prediction
国家科技期刊平台
NSTL
万方数据
