Air PM2.5 concentration multi-step forecasting using a new hybrid modeling method: Comparing cases for four cities in China

Abstract

Exposure to particulate matter (PM2.5) with high concentrations can increase the risk of human illness and mortality. Consequently, it is meaningful to build an accurate model for PM2.5 forecasting and provide reference for air pollution management and short-term warning. This paper develops a novel hybrid model called WPD-PSO-BP-Adaboost, based on WPD (Wavelet Packet Decomposition), the PSO (Particle Swarm Optimization) algorithm, BPNN (Back Propagation Neural Network) and Adaboost algorithm. In the proposed structure, to obtain better performance of PM2.5 forecasting, the novel hybrid model can be describe as: the WPD is utilized to decompose the raw PM2.5 data into several sub-layers with low frequency and high frequency; optimized by PSO and Adaboost algorithm, the BPNN is employed to compete the three-step prediction for every single subseries. To investigate the three-step forecasting performance of the proposed models, there are three experiments involving eleven models for the comparisons, including the BP model, BP-Adaboost model, WPD-BP model, PSO-BP model, WPD-BP-Adaboost model, WPD-PSO-BP model, PSO-BP-Adaboost model, WPD-PSO -BP-Adaboost model, EEMD-GRNN model, CEEMDAN-ICA-ELM model and WPD-CEEMD-PSOGSA -SVM model. The experiments results show that: (1) the WPD is useful in improving the forecasting performance; (2) the PSO and Adaboost algorithm can enhance the precision of forecasting significantly; (3) in all models, the WPD-PSO-BP-Adaboost model performs best in multi-step forecasting.