An Integrated Deep Neural Network Approach for Large-Scale Water Quality Time Series Prediction
Abstract
The prediction of water quality has great significance for the management of water environment and the protection of water resources. Traditional water quality prediction methods are relatively simple, and linear models are often used to predict water quality. However, such models limit the accuracy of prediction and lack the analysis of nonlinear characteristics of water quality. In addition, due to the complex water environment, the water quality time series has large noise, which makes it difficult for traditional models to effectively predict water quality indicators under complex environmental conditions. To solve this problem, this work proposes an integrated prediction method that combines Savitzky-Golay filter with Long Short-Term Memory (LSTM)-based Encoder-Decoder neural network to predict water quality at the next time interval. In this approach, the water quality time series is first smoothed by Savitzky-Golay filter, and LSTM can extract valid information from complex time series. Based on them, an integrated model is for the first time established and can well characterize statistical characteristics. Experimental results demonstrate that it achieves better prediction results than some typical prediction methods.