Multivariate water quality parameter prediction model based on hybrid neural network

The Yangtze River basin plays an important role in Chinese water resources allocation. What proves common knowledge is that it is particularly important to predict the water quality in the Yangtze River basin. Based on the existing research, the recurrent neural network（RNN） model with gate recurrent unit（GRU） and fully connected neural network（FCNN） are combined in this study to improve a multiple water quality parameter prediction（MWQPP） model.It is proposed to predict the four water quality parameters, such as pH, dissolved oxygen（DO）, permanganate index（CODMn） and ammonia nitrogen（NH3-N） in the Yangtze River basin. Based on 7 566 raw data of 23 water quality monitoring points in the Yangtze River basin from 2011 to 2018, the comparative experiments show that the root mean square error（RMSE）, mean absolute error（MAE）, mean absolute percentage error（MAPE） and coefficient of determination（R2） obtained from the MWQPP model's prediction results are better than traditional models, such as the multiple linear re