Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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Machine Learning-based Estimation of the Concentration of Fine Particulate Matter Using Domain Adaptation Method

Domain Adaptation 방법을 이용한 기계학습 기반의 미세먼지 농도 예측

  • Kang, Tae-Cheon (Dept. of Digital Media, Graduate School, Catholic University of Korea) ;
  • Kang, Hang-Bong (Dept. of Digital Media, Graduate School, Catholic University of Korea)
  • Received : 2017.07.19
  • Accepted : 2017.07.27
  • Published : 2017.08.31
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Abstract

Recently, people's attention and worries about fine particulate matter have been increasing. Due to the construction and maintenance costs, there are insufficient air quality monitoring stations. As a result, people have limited information about the concentration of fine particulate matter, depending on the location. Studies have been undertaken to estimate the fine particle concentrations in areas without a measurement station. Yet there are limitations in that the estimate cannot take account of other factors that affect the concentration of fine particle. In order to solve these problems, we propose a framework for estimating the concentration of fine particulate matter of a specific area using meteorological data and traffic data. Since there are more grids without a monitor station than grids with a monitor station, we used a domain adversarial neural network based on the domain adaptation method. The features extracted from meteorological data and traffic data are learned in the network, and the air quality index of the corresponding area is then predicted by the generated model. Experimental results demonstrate that the proposed method performs better as the number of source data increases than the method using conditional random fields.

Keywords

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