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Int. J. Environ. Res. Public Health 2017, 14(7), 764; https://doi.org/10.3390/ijerph14070764

Day-Ahead PM2.5 Concentration Forecasting Using WT-VMD Based Decomposition Method and Back Propagation Neural Network Improved by Differential Evolution

1
School of Economics and Management, China University of Geosciences, Wuhan 430074, China
2
Mineral Resource Strategy and Policy Research Center, China University of Geosciences, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Received: 11 May 2017 / Revised: 1 July 2017 / Accepted: 7 July 2017 / Published: 12 July 2017
(This article belongs to the Section Environmental Health)

Abstract

Accurate PM2.5 concentration forecasting is crucial for protecting public health and atmospheric environment. However, the intermittent and unstable nature of PM2.5 concentration series makes its forecasting become a very difficult task. In order to improve the forecast accuracy of PM2.5 concentration, this paper proposes a hybrid model based on wavelet transform (WT), variational mode decomposition (VMD) and back propagation (BP) neural network optimized by differential evolution (DE) algorithm. Firstly, WT is employed to disassemble the PM2.5 concentration series into a number of subsets with different frequencies. Secondly, VMD is applied to decompose each subset into a set of variational modes (VMs). Thirdly, DE-BP model is utilized to forecast all the VMs. Fourthly, the forecast value of each subset is obtained through aggregating the forecast results of all the VMs obtained from VMD decomposition of this subset. Finally, the final forecast series of PM2.5 concentration is obtained by adding up the forecast values of all subsets. Two PM2.5 concentration series collected from Wuhan and Tianjin, respectively, located in China are used to test the effectiveness of the proposed model. The results demonstrate that the proposed model outperforms all the other considered models in this paper. View Full-Text
Keywords: PM2.5 concentration forecasting; wavelet transform; variational mode decomposition; differential evolution; back propagation neural network PM2.5 concentration forecasting; wavelet transform; variational mode decomposition; differential evolution; back propagation neural network
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Wang, D.; Liu, Y.; Luo, H.; Yue, C.; Cheng, S. Day-Ahead PM2.5 Concentration Forecasting Using WT-VMD Based Decomposition Method and Back Propagation Neural Network Improved by Differential Evolution. Int. J. Environ. Res. Public Health 2017, 14, 764.

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