Deep Learning-Based Water Quality Parameter Prediction

Water quality prediction is the basis and premise of environmental protection and water resource management. However, due to the complex nonlinear and dynamic nature of water quality data, accurate prediction faces many challenges. Therefore, a hybrid model for short-term water quality parameter prediction based on multiplicative decomposition model and sample convolution exchange network (SCINet) is proposed in this paper. Through experiments, MD_SCINet prediction model can effectively extract water quality characteristics and make time series prediction. The experimental results show that the prediction error of this model is significantly lower than that of TCN and CNN-LSTM, and the mean absolute error (MAE) is 35% and 44% lower than that of TCN and CNN-LSTM, respectively. The results show that this method is effective in short-term water quality parameter prediction and provides effective technical support for environmental protection and water resources management.