Study on the Rapid Prediction Model of Water Quality for Emergency Water Pollution

Abstract

Water quality is a basic work in environmental governance, which has vital significance in promoting the sustainable utilization of water resources and instant pollution prevention and precise control. Water quality data is dynamic and frequently fluctuating with different temporal and spatial dimensions, therefore it can be challenging to predict. A hybrid AM-ConvLSTM deep learning algorithm is proposed in this paper to rapidly predict the trend of water quality which can run faster and require low computing power rather than the traditional MIKE 21 hydrological method. The ConvLSTM method and the attention mechanism are assembled to build AM-ConvLSTM model to better capture spatial correlation. Moreover, the statistic methods are used to evaluate the effectiveness of the model and then compared with varieties of deep learning baseline methods. The results reveal that the hybrid AM-ConvLSTM model can effectively replace MIKE 21 model to predict the future trend of water quality, and then the local environmental protection agencies will respond quickly to emergency water pollution.