A decomposition and ensemble model based on GWO and Differential Evolution algorithm for PM 2.5 concentration forecasting

Abstract

Accurate and reliable prediction of PM_2.5 concentrations is the basis for appropriate warning measures, and a single prediction model is often ineffective. In this paper, we propose a novel decomposition-and-ensemble model to predict the concentration of PM_2.5. The model utilizes Ensemble Empirical Mode Decomposition (EEMD) to decompose PM_2.5 series, Support Vector Regression (SVR) to predict each Intrinsic Mode Function (IMF), and a hybrid algorithm based on Differential Evolution (DE) and Grey Wolf Optimizer (GWO) to optimize SVR parameters. The proposed prediction model EEMD-SVR-DEGWO is employed to forecast the concentration of PM2.5 in Guangzhou, Wuhan, and Chongqing of China. Compared with six prediction models, the proposed EEMD-SVR-DEGWO is a reliable predictor and has achieved competitive results.

Keywords

prediction decomposition-and-ensemble support vector regression

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