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A decomposition and ensemble model based on GWO and Differential Evolution algorithm for PM2.5 concentration forecasting.
- Source :
- Journal of Intelligent & Fuzzy Systems; 2023, Vol. 45 Issue 2, p2497-2512, 16p
- Publication Year :
- 2023
-
Abstract
- Accurate and reliable prediction of PM<subscript>2.5</subscript> 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<subscript>2.5</subscript>. The model utilizes Ensemble Empirical Mode Decomposition (EEMD) to decompose PM<subscript>2.5</subscript> 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10641246
- Volume :
- 45
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Journal of Intelligent & Fuzzy Systems
- Publication Type :
- Academic Journal
- Accession number :
- 170719025
- Full Text :
- https://doi.org/10.3233/JIFS-230343