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Deep learning architecture for air quality predictions

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Abstract

With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed. A stacked autoencoder (SAE) model is used to extract inherent air quality features, and it is trained in a greedy layer-wise manner. Compared with traditional time series prediction models, our model can predict the air quality of all stations simultaneously and shows the temporal stability in all seasons. Moreover, a comparison with the spatiotemporal artificial neural network (STANN), auto regression moving average (ARMA), and support vector regression (SVR) models demonstrates that the proposed method of performing air quality predictions has a superior performance.

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Acknowledgments

This research was sponsored by the National Science-technology Support Plan Project of China (Grant Nos. 2015BAJ02B00 and 2015BAJ02B03).

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Authors and Affiliations

  1. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100094, China

    Xiang Li, Ling Peng, Yuan Hu & Tianhe Chi

  2. Institute of Electronics, Chinese Academy of Sciences, Beijing, 100190, China

    Jing Shao

  3. University of Chinese Academy of Sciences, Beijing, 100049, China

    Xiang Li & Yuan Hu

Authors
  1. Xiang Li
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  2. Ling Peng
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  4. Jing Shao
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  5. Tianhe Chi
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Correspondence to Ling Peng.

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Responsible editor: Marcus Schulz

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Li, X., Peng, L., Hu, Y. et al. Deep learning architecture for air quality predictions. Environ Sci Pollut Res 23, 22408–22417 (2016). https://doi.org/10.1007/s11356-016-7812-9

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