Journal of Information Technology Services (한국IT서비스학회지)

Korea Society of IT Services (한국IT서비스학회)
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An Estimation Model of Fine Dust Concentration Using Meteorological Environment Data and Machine Learning

기상환경데이터와 머신러닝을 활용한 미세먼지농도 예측 모델

  • 임준묵 (한밭대학교 공과대학 창의융합학과)
  • Received : 2018.11.08
  • Accepted : 2019.01.26
  • Published : 2019.03.31
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Abstract

Recently, as the amount of fine dust has risen rapidly, our interest is increasing day by day. It is virtually impossible to remove fine dust. However, it is best to predict the concentration of fine dust and minimize exposure to it. In this study, we developed a mathematical model that can predict the concentration of fine dust using various information related to the weather and air quality, which is provided in real time in 'Air Korea (http://www.airkorea.or.kr/)' and 'Weather Data Open Portal (https://data.kma.go.kr/).' In the mathematical model, various domestic seasonal variables and atmospheric state variables are extracted by multiple regression analysis. The parameters that have significant influence on the fine dust concentration are extracted, and using ANN (Artificial Neural Network) and SVM (Support Vector Machine), which are machine learning techniques, we proposed a prediction model. The proposed model can verify its effectiveness by using past dust and weather big data.

Keywords

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Weather Data Station Location Map (https://data.kma.go.kr/)

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Location of fine Dust Meter Station (http://www.airkorea.or.kr/)

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Estimation and Actual Value Distribution According to Regression Equation

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ANN Model for Prediction of Fine Dust Concentration

Weather Related Data(https://data.kma.go.kr/)

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Fine Dust Related Data(Air Korea)

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Multi-Collinearity Analysis Result

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Multi-Regression Analysis Result of Fine Dust Estimation

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Accuracy of Multiple Regression Analysis Prediction Model

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Result of Fine Dust Prediction by SVM

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Accuracy of SVM Prediction Model

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Result of Fine Dust Prediction by ANN

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Accuracy of ANN Prediction Model

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Accuracy Comparison of Multiple Regression, SVM, and ANN Prediction Models

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Multiple Regression Analysis

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References

  1. Cha, J.W. and J.Y. Kim, "Development of Data Mining Algorithm for Implementation of Fine Dust Numerical Prediction Model", Journal of the Korea Institute of Information and Communication Engineering, Vol.22, No.4, 2018, 595-601. https://doi.org/10.6109/JKIICE.2018.22.4.595
  2. Cortes, C. and V. Vapnik, "Support-vector networks", Machine Learning, Vol.20, No.3, 1995, 273-297. https://doi.org/10.1007/BF00994018
  3. Hagan, M.T., H.B. Demuth, M.H. Beale, and O. Jesus, Neural Network Design (2nd Edition), Martin Hagan, 2014.
  4. Joun, S.W., J.Y. Choi, and J.H. Bae, "Performance Comparison of Algorithms for the Prediction of Fine Dust Concentration", Proceedings of Korea Computer Congress, The Korean Institute of Information Scientists and Engineers, No.12, 2017, 775-777.
  5. Kang, T.C. and H.B. Kang, "Machine Learningbased Estimation of the Concentration of Fine Particulate Matter Using Domain Adaptation Method", Journal of Korea Multimedia Society, Vol.20, No.8, 2017, 1208-1215. https://doi.org/10.9717/KMMS.2017.20.8.1208
  6. Koo, Y.S., H.Y. Yun, H.Y. Kwon, and S.H. Yu, "A Development of PM10 Forecasting System", Journal of Korean Society for Atmospheric Environment, Vol.26, No.6, 2010, 666-682. https://doi.org/10.5572/KOSAE.2010.26.6.666
  7. Lee, J.G., Multivariate Analysis and Data Mining with R, Hwangso Academy, 2016.
  8. Lee, M.H., Korea-China collaborative study to abate trans-boundary air pollution(II), National Institute of Environmental Research, Research Report, 2016. 6.
  9. Liu, Z., M.J. Zuo, X. Zhao, and H. Xu, "An Analytical Approach to Fast Parameter Selection of Gaussian RBF Kernel for Support Vector Machine", Journal of Information Science and Engineering, Vol.31, No.2, 2015, 691-710.
  10. Oh, B.D., J.H. Park, and Y.S. Kim, "Prediction of the concentration of PM10 using Machine-Learning", Proceedings of The Korean Institute of Information Scientists and Engineers Winter Conferences, No.12, 2016, 1674-1676.
  11. Oh, J.M., H.S. Shin, Y.S. Shin, and H.C. Jeong, "Forecasting the Particulate Matter in Seoul using a Univariate Time Series Approach", Journal of the Korean Data Analysis Society, Vol.19, No.5, 2017, 2457-2468. https://doi.org/10.37727/jkdas.2017.19.5.2457
  12. Shin, E.K., J. Kim, and Y. Choi, "A Study on the Data Model Design of Fine Dust Related Disease", Journal of The Korea Society of Information Technology Policy & Management, Vol.10, No.1, 2018, 655-659.
  13. Shin, M.K., C.D. Lee, H.S. Ha, C.S. Choe, and Y.H. Kim, "The Influence of Meteorological Factors on PM10 Concentration in Incheon", Journal of Korean Society for Atmospheric Environment, Vol.23, No.3, 2007, 322-331. https://doi.org/10.5572/KOSAE.2007.23.3.322
  14. Tofallis, C., "A Better Measure of Relative Prediction Accuracy for Model Selection and Model Estimation", Journal of the Operational Research Society, Vol.66, No.8, 2016, 1352-1362. https://doi.org/10.1057/jors.2014.103