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Air pollutant concentration prediction based on a new hybrid model, feature selection, and secondary decomposition

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Abstract

The concentration of air pollutants is closely related to people’s production and life. Air quality prediction is the premise for environmental management departments to make decisions and put forward pollution control measures. A novel air pollutant prediction model was proposed in this paper to predict air pollutant concentration more accurately. Firstly, the data were decomposed into several subsequences by a complete ensemble empirical mode decomposition with adaptive noise and calculated the sample entropy of the subsequence. Secondly, variational mode decomposition is used to decompose the sequence with the highest sample entropy, and a fast correlation-based filter is used to select the features of the second decomposed sequence and the remaining sequences. Then, a multi-layer perceptron is used to predict the processed quadratic decomposition sequence, and a gated recurrent unit is used to predict the remaining sequences. According to the experimental results, three main conclusions can be drawn. First, through two groups of comparative experiments, it is found that the model has a good prediction effect. Second, after adding the decomposition algorithm, the average improvement levels of mean absolute error and root mean squared error were 44.50% and 34.77%, respectively. Third, after the re-decomposition of intrinsic mode functions 1, the mean absolute percentage error can be reduced by 22.98% on average on the original basis. The results of this study can provide a valuable reference for the prediction of atmospheric pollutants.

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Data availability

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

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Authors and Affiliations

  1. Department of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding, 071000, China

    Weijun Wang & Tianyu Ma

  2. Engineering Management Branch of State Grid, Fengtai District, Jibei Electric Power Company Limited, 188 South 4Th Ring West Road, Beijing, 100071, China

    Lianru Wang

Authors
  1. Weijun Wang
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  2. Tianyu Ma
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Contributions

Weijun Wang: conceptualization, methodology, and supervision. Tianyu Ma: data curation and writing—original draft preparation. Lianru Wang: software, performing the experiments, and writing—reviewing and editing.

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Correspondence to Lianru Wang.

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Wang, W., Ma, T. & Wang, L. Air pollutant concentration prediction based on a new hybrid model, feature selection, and secondary decomposition. Air Qual Atmos Health 16, 2019–2033 (2023). https://doi.org/10.1007/s11869-023-01388-z

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