Design of a Combined System Based on Multi-Objective Optimization for Fine Particulate Matter (PM2.5) Prediction

Abstract

Air pollution forecasting plays a pivotal role in environmental governance, so a large number of scholars have devoted themselves to the study of air pollution forecasting models. Although numerous studies have focused on this field, they failed to consider fully the linear feature, non-linear feature, and fuzzy features contained in the original series. To fill this gap, a new combined system is built to consider features in the original series and accurately forecast PM2.5 concentration, which incorporates an efficient data decomposition strategy to extract the primary features of the PM2.5 concentration series and remove the noise component, and five forecasting models selected from three types of models to obtain the preliminary forecasting results, and a multi-objective optimization algorithm to combine the prediction results to produce the final prediction values. Empirical studies results indicated that in terms of RMSE the developed combined system achieves 0.652 6%, 0.810 1%, and 0.775 0% in three study cities, respectively. Compared to other prediction models, the RMSE improved by 60% on average in the study cities.