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Multi-scale spatiotemporal graph convolution network for air quality prediction

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Abstract

Air pollution is a serious environmental problem that has attracted much attention. Air quality prediction can provide useful information for urban environmental governance decision-making and residents’ daily health control. However, existing research methods have suffered from a weak ability to capture the spatial correlations and fail to model the long-term temporal dependencies of air quality. To overcome these limitations, we propose a multi-scale spatiotemporal graph convolution network (MST-GCN), which consists of a multi-scale block, several spatial-temporal blocks and a fusion block. We first divide the extracted features into several groups based on their domain categories, and represent the spatial correlations across stations as two graphs. Then we combine the grouped features and the constructed graphs in pairs to form a multi-scale block that feeds into spatial-temporal blocks. Each spatial-temporal block contains a graph convolution layer and a temporal convolution layer, which can model the spatial correlations and long-term temporal dependencies. To capture the group interactions, we use a fusion block to fuse multiple groups. Extensive experiments on a real-world dataset demonstrate that our model achieves the highest performance compared with state-of-the-art and baseline models for air quality prediction.

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Notes

  1. https://www.who.int/air-pollution/news-and-events/how-air-pollution-is-destroying-our-health

  2. http://www.bjmemc.com.cn/

  3. https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/global-data-assimilation-system-gdas

  4. https://lbs.amap.com/api/webservice/guide/api/search

  5. http://www.openstreetmap.org/

  6. https://en.wikipedia.org/wiki/Cosine_similarity

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Authors and Affiliations

  1. College of Computer Science, Chongqing University, Chongqing, 400000, China

    Liang Ge, Kunyan Wu, Yi Zeng, Feng Chang, Yaqian Wang & Siyu Li

  2. Chongqing Key Laboratory of Software Theory Technology, Chongqing, 400000, China

    Liang Ge, Kunyan Wu, Feng Chang, Yaqian Wang & Siyu Li

Authors
  1. Liang Ge
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  2. Kunyan Wu
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  3. Yi Zeng
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  4. Feng Chang
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  5. Yaqian Wang
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  6. Siyu Li
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Correspondence to Liang Ge.

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

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Ge, L., Wu, K., Zeng, Y. et al. Multi-scale spatiotemporal graph convolution network for air quality prediction. Appl Intell 51, 3491–3505 (2021). https://doi.org/10.1007/s10489-020-02054-y

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