Prediction of river water quality considering spatiotemporal correlation and meteorological factors

Abstract

With the rapid development of economy, the problem of river pollution is becoming more and more serious. It is very important and challenging to build a high-precision water quality prediction model for the comprehensive management of water environment and prevention of water pollution. At present, some achievements have been made in data-driven river water quality prediction, mainly including grey model, time series model, support vector machine model and neural network model. However, these existing prediction methods are limited to single station prediction. The influence of the upstream water quality on the downstream water quality is ignored and the upstream stations at different locations will have different effects on the downstream water quality. In order to solve the problem that the traditional model of water quality prediction does not consider the upstream influence and difficulties in long-term prediction, a new water quality prediction model based on spatiotemporal attention mechanism and long-short-term memory neural network (TS-Attention-LSTM) was proposed, which considers the spatiotemporal correlation and meteorological factors. Firstly, the spatial attention module was embedded in the encoder to extract the significant spatial correlation between upstream and downstream. The interaction among the water quality indicators was also extracted. Then, the temporal attention module was embedded in the decoder to extract the important time series features. Moreover, the meteorological factors and spatial characteristics were fused in the decoder. Finally, the multi-step prediction of water quality was carried out by using LSTM model. In this paper, the research area located in Jinjiang River Watershed in Fujian Province, China. The nonpoint source pollution in this river basin mainly interrelated with livestock discharge, agricultural field runoff and rural domestic sewage. The results show that the TS-Attention-LSTM model can effectively capture the spatial and temporal characteristics of water quality index (such as dissolved oxygen, DO and total phosphorus, TP) and the influence of meteorological factors in Jinjiang River Basin. The mean absolute error (MAE) of the TS-Attention-LSTM model was 0.24, the mean absolute percent error (MAPE) was 3.36%, and the root mean square error (RMSE) was 0.32, which performed best in all comparison models. The determinable coefficient R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> was 0.5062, performed second best in all comparison models.