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A hybrid deep learning technology for PM2.5 air quality forecasting

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Abstract

The concentration of PM2.5 is one of the main factors in evaluating the air quality in environmental science. The severe level of PM2.5 directly affects the public health, economics and social development. Due to the strong nonlinearity and instability of the air quality, it is difficult to predict the volatile changes of PM2.5 over time. In this paper, a hybrid deep learning model VMD-BiLSTM is constructed, which combines variational mode decomposition (VMD) and bidirectional long short-term memory network (BiLSTM), to predict PM2.5 changes in cities in China. VMD decomposes the original PM2.5 complex time series data into multiple sub-signal components according to the frequency domain. Then, BiLSTM is employed to predict each sub-signal component separately, which significantly improved forecasting accuracy. Through a comprehensive study with existing models, such as the EMD-based models and other VMD-based models, we justify the outperformance of the proposed VMD-BiLSTM model over all compared models. The results show that the prediction results are significantly improved with the proposed forecasting framework. And the prediction models integrating VMD are better than those integrating EMD. Among all the models integrating VMD, the proposed VMD-BiLSTM model is the most stable forecasting method.

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Data availability

The data used in this paper is publicly available at UCI machine learning knowledge base. URL: http://archive.ics.uci.edu/ml/

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Acknowledgements

Authors from the original publication of the dataset (Zhang et al. 2017) are appreciated.

Funding

This work was supported by the Ministry of Education (MOE) Singapore, Tier 1 Grant for National University of Singapore (NUS) under grant number R296000208133.

Author information

Authors and Affiliations

  1. Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, Hangzhou, 310018, China

    Zhendong Zhang, Yongkang Zeng & Ke Yan

  2. National University of Singapore, Singapore, 117566, Singapore

    Ke Yan

Authors
  1. Zhendong Zhang
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  2. Yongkang Zeng
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  3. Ke Yan
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Contributions

Conceptualization, K.Y.; methodology, K.Y.; software, Z.Z.; validation, Y.Z.; formal analysis, Z.Z.; investigation, K.Y.; resources, K.Y.; original draft preparation, K.Y.; writing, review and editing, K.Y.; visualization, Y.Z.; supervision, K.Y.; project administration, K.Y.; funding acquisition, K.Y.

Corresponding author

Correspondence to Ke Yan.

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Ethics Committee approval was received from the research ethics committees in the College of Information Engineering of China Jiliang University and National University of Singapore.

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The authors declare that they have no competing interest.

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Zhang, Z., Zeng, Y. & Yan, K. A hybrid deep learning technology for PM2.5 air quality forecasting. Environ Sci Pollut Res 28, 39409–39422 (2021). https://doi.org/10.1007/s11356-021-12657-8

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  • DOI: https://doi.org/10.1007/s11356-021-12657-8

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