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A new decomposition-integrated air quality index prediction model

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Abstract

Air quality has a significant impact on human health, in order to alleviate the air pollution and improve the ability to predict the air quality. In this paper, a prediction model of air quality index composed of variational mode decomposition and temporal convolutional network was proposed. First, in order to reduce the non-stationarity and randomness of the time series, the original air quality index sequence was decomposed by variational mode decomposition, and the decomposition number was determined by the central frequency method to decompose into multiple relatively stable sub-sequences with different frequency scales. Then, the decomposed sub-stable sequence was predicted by the time convolutional network. Finally, the prediction data were integrated and reconstructed to obtain the final prediction results. Comparing the results of other forecasting models by performance evaluation metrics, the combined forecasting model proposed in this paper reduced RMSE by 20.9%, 19.2%, 5.1%, 29.9%, 23.7% on the Beijing dataset. MAPE reduced by 26.6%, 22.3%, 19.5%, 28.9%, 15.0%, respectively. MAE decreased by 19.1%, 20.6%, 9.6%, 29.5%, 23.5%. R2 increased by 4.6%, 4.0%, 0.8%, 14.9%, 5.5% respectively. This proves the accuracy and reliability of the proposed model.

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The source data used to support the findings of this study is available from the corresponding author upon request.

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Funding

This paper is supported by the Natural Science Foundation of Liaoning Province of China (No. 2020-MS-210), the Science Research Project of Liaoning Education Department (No. LJKZ0143) and the Applied Basic Research Program Project of Liaoning Province (No. 2022JH2/101300246).

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Authors and Affiliations

  1. College of Artificial Intelligence, Shenyang University of Technology, Shenyang, 110870, China

    Xiaolei Sun, Zhongda Tian & Zhijia Zhang

  2. Shenyang Key Laboratory of Information Perception and Edge Computing, Shenyang University of Technology, Shenyang, 110870, China

    Zhongda Tian & Zhijia Zhang

Authors
  1. Xiaolei Sun
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  2. Zhongda Tian
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  3. Zhijia Zhang
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Contributions

Xiaolei Sun: Software, Validation, Writing.

Zhongda Tian: Conceptualization, Methodology, Software, Validation, Writing, and Funding acquisition.

Zhijia Zhang: Software and Validation.

Corresponding author

Correspondence to Zhongda Tian.

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The manuscript was vetted and approved for publication by all authors.

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The authors declare no competing interests.

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Communicated by: H. Babaie

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Sun, X., Tian, Z. & Zhang, Z. A new decomposition-integrated air quality index prediction model. Earth Sci Inform 16, 2307–2321 (2023). https://doi.org/10.1007/s12145-023-01028-1

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  • DOI: https://doi.org/10.1007/s12145-023-01028-1

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