Real-Time River Water Quality Prediction Model Based on Spatial Correlation and Neural Network Model

Based on the high frequency water quality online monitoring data, the spatial correlation of water quality data was used to construct a neural network model to realize the real-time prediction of river water quality. The model was applied to the Baini River Basin in Guangzhou, and the water quality parameters of dissolved oxygen and ammonia nitrogen were predicted and analyzed to verify the effect of the model. According to different prediction time, six water quality prediction models were built, and the results showed that the model predicting dissolved oxygen 6 hours in advance had better prediction effect, while the model predicting ammonia nitrogen 24 hours in advance had better effect. The average absolute errors of the better trained model for real-time water quality prediction of dissolved oxygen and ammonia nitrogen were 0.43 mg/L and 0.29 mg/L, respectively, and the root mean square errors were 0.71 mg/ L and 0.36 mg/L, respectively. At 95% confidence level, the prediction interval coverage rates were 96.6% and 97% respectively. The model can be used as the early warning of abnormal water quality events. At the same time, the sensitivity analysis of the input items by the model can be used to analyze the pollution sources to help the basin identify the main sources of pollutants. © 2022 Peking University.