A Novel Air Pollutant Concentration Prediction System Based on Decomposition-Ensemble Mode and Multi-Objective Optimization for Environmental System Management

Abstract

With the continuous expansion of the industrial production scale and the rapid promotion of urbanization, more and more serious air pollution threatens people’s lives and social development. To reduce the losses caused by polluted weather, it is popular to predict the concentration of pollutants timely and accurately, which is also a research hotspot and challenging issue in the field of systems engineering. However, most studies only pursue the improvement of prediction accuracy, ignoring the function of robustness. To make up for this defect, a novel air pollutant concentration prediction (APCP) system is proposed for environmental system management, which is constructed by four modules, including time series reconstruction, submodel simulation, weight search, and integration. It not only realizes the filtering and reconstruction of redundant series based on the decomposition-ensemble mode, but also the weight search mechanism is designed to trade off precision and stability. Taking the hourly concentration of PM2.5 in Guangzhou, Shanghai, and Chengdu, China as an example, the simulation results show that the APCP system has perfect prediction capacity and superior stability performance, which can be used as an effective tool to guide early warning decision-making in the management of environmental engineering.