Multi-indicator Water Quality Prediction with ProbSparse Self-attention and Generative Decoder

Abstract

Water quality prediction refers to the prediction of future water quality changes based on past data. Traditional prediction models cannot capture intricate and nonlinear features. Typical machine learning methods extract nonlinear characteristics, but they suffer from overfitting problems due to data noise. Most current deep learning models have problems of gradient disappearance and explosion, and often fail to capture long-term dependence. To solve above-mentioned problems, this work proposes a multi-indicator time series prediction method named SG-Informer for river water quality prediction. SG-Informer integrates the Savitsky-Golay filter, the ProbSparse self-attention mechanism of an encoder, and a generative style decoder, serving as data smoothing and noise elimination, network scale reduction, and prediction speed improvement, respectively. SG-Informer establishes a high-quality water quality time prediction model, which effectively predicts the future water quality time series trend. Based on real-life data sets of water quality, multi-indicator and single-indicator prediction experiments are performed. Experimental results demonstrate that the proposed SG-Informer outperforms several state-of-the-art prediction methods in terms of prediction accuracy.