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Short-Term Air Quality Prediction Based on Fractional Grey Linear Regression and Support Vector Machine

Author

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  • Meng Dun
  • Zhicun Xu
  • Yan Chen
  • Lifeng Wu

Abstract

To predict the daily air pollutants, the fractional multivariable model is established. The hybrid model of the grey multivariable regression model with fractional order accumulation model (FGM(0, m)) and support vector regression model (SVR) is used to predict the air pollutants (PM 10 , PM 2.5 , and NO 2 ) from December 31, 2018, to January 3, 2019, in Shijiazhuang and Chongqing. The absolute percentage errors (APEs) are used to determine the weights of the FGM(0, m) and SVR. Meanwhile, the Holt–Winters model is used to predict the air quality pollutants for the same location and period. When the mean absolute percent error (MAPE) is 0%–20%, it indicates that the model has good accuracy of fitting and prediction. The MAPE of the hybrid model is less than 20%. It is shown that except for the PM 2.5 concentration prediction in Shijiazhuang (13.7%), the MAPE between the forecasting and actual values of the three air pollutants in Shijiazhuang and Chongqing was less than 10%.

Suggested Citation

  • Meng Dun & Zhicun Xu & Yan Chen & Lifeng Wu, 2020. "Short-Term Air Quality Prediction Based on Fractional Grey Linear Regression and Support Vector Machine," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-13, May.
    • Handle: RePEc:hin:jnlmpe:8914501
    DOI: 10.1155/2020/8914501
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    Cited by:

    1. Yuan Huang & Junhao Yu & Xiaohong Dai & Zheng Huang & Yuanyuan Li, 2022. "Air-Quality Prediction Based on the EMD–IPSO–LSTM Combination Model," Sustainability, MDPI, vol. 14(9), pages 1-18, April.
    2. Şahin, Utkucan & Ballı, Serkan & Chen, Yan, 2021. "Forecasting seasonal electricity generation in European countries under Covid-19-induced lockdown using fractional grey prediction models and machine learning methods," Applied Energy, Elsevier, vol. 302(C).

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