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A U-net Architecture Based Model for Precise Air Pollution Concentration Monitoring

In: AI and Analytics for Smart Cities and Service Systems

Author

Listed:
  • Feihong Wang

    (Insights Value Technology)

  • Gang Zhou

    (Insights Value Technology)

  • Yaning Wang

    (Insights Value Technology)

  • Huiling Duan

    (Insights Value Technology)

  • Qing Xu

    (Insights Value Technology)

  • Guoxing Wang

    (Insights Value Technology)

  • Wenjun Yin

    (Insights Value Technology)

Abstract

Convolutional Neural Network (CNN) is one of the main deep learning algorithms that has gained increasing popularity in a variety of domains across the globe. In this paper, we use U-net, one of the CNN architectures, to predict spatial PM2.5 concentrations for each 500 m × 500 m grid in Beijing. Different aspects of data including satellite data, meteorological data, high density PM2.5 monitoring data and topography data were taken into consideration. The temporal and spatial distribution patterns of PM2.5 concentrations can be learned from the result. Then, a customized threshold was added for each predicted grid PM2.5 concentration to define high-value areas to find precise location of potential PM2.5 discharge events.

Suggested Citation

  • Feihong Wang & Gang Zhou & Yaning Wang & Huiling Duan & Qing Xu & Guoxing Wang & Wenjun Yin, 2021. "A U-net Architecture Based Model for Precise Air Pollution Concentration Monitoring," Lecture Notes in Operations Research, in: Robin Qiu & Kelly Lyons & Weiwei Chen (ed.), AI and Analytics for Smart Cities and Service Systems, pages 65-75, Springer.
    • Handle: RePEc:spr:lnopch:978-3-030-90275-9_6
    DOI: 10.1007/978-3-030-90275-9_6
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