Research on water quality spatiotemporal forecasting model based on ST-BIGRU-SVR neural network

Abstract

Abstract With the serious deterioration of the water environment, accurate prediction of water quality changes has become a topic of increasing concern. To further improve the accuracy of water quality prediction and the stability and generalization ability of the model, we propose a new water quality spatiotemporal forecast model to predict future water quality. To capture the spatiotemporal characteristics of water quality pollution data, the three sites (station S1, station S2, station S4) with the highest temperature time series concentration correlation at the experimental sites were first extracted to predict the water temperature at station S1, and 17,380 records were collected at each monitoring station, and the spatiotemporal characteristics were extracted by BiGRU-SVR network model. This paper's prediction test is based on the actual water quality data of the Qinhuangdao sea area in Hebei province from 2 September to 26 September 2013 and compared with other baseline models. The experimental results show that the proposed model is better than other baseline models and effectively improves the accuracy of water quality prediction, and the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) are 0.071, 0.076, and 0.957, respectively, which have good robustness.