Long-term Water Quality Prediction based on Intelligent Optimization and Seasonal-trend Decomposition

Nowadays, the applications of water quality prediction in the field of regional water environment management are increasing. It refers to predicting the elemental values of the water environment in the future based on past monitoring data, which is essential to realize the real-time evaluation of water quality and dynamic control of pollution sources. However, the water environment indicators are affected by various elements, which have a large volatility and non-linear characteristics. In addition, most of the existing water quality predictions focus on single-step predictive modeling of single elements of the water environment and lack multi-step predictive analysis of multifactor data of the water environment. In this paper, a novel long-term prediction model based on genetic simulated annealing-based particle swarm optimization (GSPSO) with seasonal-trend decomposition using LOESS (STL) is proposed and named GSPSO-STL-Autoformer (GS-Autoformer). It realizes the multi-factor and long-term prediction of water quality time series data. Firstly, the Autoformer's hyperparameters are optimized by the GSPSO to improve its convergence speed. Secondly, the multi-factor features are decomposed by the STL to make the model more focused on learning feature information of each component. Finally, the long-term prediction is realized by the Autoformer. Comparative experiments with state-of-the-art peers show that the GS-Autoformer can effectively improve the accuracy of multi-factor and long-term predictions.