Large-scale water quality prediction with integrated deep neural network

Water environment time series prediction is important to efficient water resource management. Traditional water quality prediction is mainly based on linear models. However, owing to complex conditions of the water environment, there is a lot of noise in the water quality time series, which will seriously affect the accuracy of water quality prediction. In addition, linear models are difficult to deal with the nonlinear relations of data of time series. To address this challenge, this work proposes a hybrid model based on a long short-term memory-based encoder-decoder neural network and a Savitzky-Golay filter. Among them, the filter of Savitzky-Golay can eliminate the potential noise in the time series of water quality, and the long short-term memory can investigate nonlinear characteristics in a complicated water environment. In this way, an integrated model is proposed and effectively obtains statistical characteristics. Realistic data-based experiments prove that its prediction performance is better than its several state-of-the-art peers.