Research on Water Quality Prediction Based on SARIMA-LSTM: A Case Study of Beilun Estuary

Abstract

As water environment is an important part of mangrove ecosystem, an efficient prediction of water quality is the foundation for judging the health of wetland ecosystem. And it also contributes a lot to the smooth development of environmental protection work. Based on the data of water quality and weather in Beilun Estuary, this paper chooses permanganate index and the content of ammonia nitrogen, which can reflect the water quality, as forecasting targets. We propose a multi-feature prediction method called SARIMA-LSTM on the basis of seasonal autoregressive integrated moving average model and long short-term memory. Through the combination of linear and non-linear model, this method can possess a better prediction effect considering the influence of weather on water quality. And the experimental results of four models show that this method has higher accuracy, stability and reliability.