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Day-Ahead PM 2.5 Concentration Forecasting Using WT-VMD Based Decomposition Method and Back Propagation Neural Network Improved by Differential Evolution

Author

Listed:
  • Deyun Wang

    (School of Economics and Management, China University of Geosciences, Wuhan 430074, China
    Mineral Resource Strategy and Policy Research Center, China University of Geosciences, Wuhan 430074, China)

  • Yanling Liu

    (School of Economics and Management, China University of Geosciences, Wuhan 430074, China)

  • Hongyuan Luo

    (School of Economics and Management, China University of Geosciences, Wuhan 430074, China)

  • Chenqiang Yue

    (School of Economics and Management, China University of Geosciences, Wuhan 430074, China)

  • Sheng Cheng

    (School of Economics and Management, China University of Geosciences, Wuhan 430074, China
    Mineral Resource Strategy and Policy Research Center, China University of Geosciences, Wuhan 430074, China)

Abstract

Accurate PM 2.5 concentration forecasting is crucial for protecting public health and atmospheric environment. However, the intermittent and unstable nature of PM 2.5 concentration series makes its forecasting become a very difficult task. In order to improve the forecast accuracy of PM 2.5 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 PM 2.5 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 PM 2.5 concentration is obtained by adding up the forecast values of all subsets. Two PM 2.5 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.

Suggested Citation

  • Deyun Wang & Yanling Liu & Hongyuan Luo & Chenqiang Yue & Sheng Cheng, 2017. "Day-Ahead PM 2.5 Concentration Forecasting Using WT-VMD Based Decomposition Method and Back Propagation Neural Network Improved by Differential Evolution," IJERPH, MDPI, vol. 14(7), pages 1-22, July.
    • Handle: RePEc:gam:jijerp:v:14:y:2017:i:7:p:764-:d:104457
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    References listed on IDEAS

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    Cited by:

    1. Li, Jieyi & Qian, Shuangyue & Li, Ling & Guo, Yuanxuan & Wu, Jun & Tang, Ling, 2024. "A novel secondary decomposition method for forecasting crude oil price with twitter sentiment," Energy, Elsevier, vol. 290(C).
    2. Li, Guohui & Yin, Shibo & Yang, Hong, 2022. "A novel crude oil prices forecasting model based on secondary decomposition," Energy, Elsevier, vol. 257(C).
    3. Hengliang Guo & Yanling Guo & Wenyu Zhang & Xiaohui He & Zongxi Qu, 2021. "Research on a Novel Hybrid Decomposition–Ensemble Learning Paradigm Based on VMD and IWOA for PM 2.5 Forecasting," IJERPH, MDPI, vol. 18(3), pages 1-19, January.

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