International journal of advanced smart convergence

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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Prediction of changes in fine dust concentration using LSTM model

  • Received : 2022.03.14
  • Accepted : 2022.03.20
  • Published : 2022.06.30
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Abstract

Because fine dust (PM10) has a close effect on the environment, fine dust generated in the climate and living environment has a bad effect on the human body. In this study, the LSTM model was applied to predict and analyze the effect of fine dust on Gwangju Metropolitan City in Korea. This paper uses prediction values of input variables selected through correlation analysis to confirm fine dust prediction performance. In this paper, data from the Gwangju Metropolitan City area were collected to measure fine dust. The collection period is one year's worth of data was used from january to December of 2021, and the test data was conducted using three-month data from January to March of 2022. As a result of this study, in the as a result of predicting fine dust (PH10) and ultrafine dust (PH2.5) using the LSTM model, the RMSE was 4.61 and the test result value was as low as 4.37. This reason is judged to be the result of the contents of the one-year sample.

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References

  1. S. Fuzzi, U. Baltensperger, K. Carslaw, S. Decesari, H. Denier vander Gon, M.C. Facchini, D. Fowler, I. Koren, B. Langford, U.Lohmann, E. Nemitz, S. Pandis, I. Riipinen, Y. Rudich, M. Schaap, J. G. Slowik, D. V. Spracklen, E. Vignati, M. Wild, M. Williams, and S. Gilardoni, "Particulate matter, air quality and climate: Lessons learned and future needs," Atmospheric chemistry and physics, Vol. 15, No. 14, pp.8217-8299, 2015. DOI: https://doi.org/10.5194/acp-15-8217-2015
  2. N. J. Hime, G. B. Marks, and C. T. Cowie, "A comparison of the health effects of ambient particulate matter air pollution from five emission sources," International Journal of Environmenta Research and Public Health, Vol. 15, No. 6, 2018. DOI: https://doi.org/10.3390/ijerph15061206
  3. Organization for Economic Co-operation and Development (OECD), The economic consequences of outdoor air pollution, Policy Highlights, 2016.
  4. H.-S. Choi, M. Kang, Y. C. Kim, and H. Choi, "Deep Learning-based Prediction of PM10 Fluctuation from Gwanak-gu Urban Area, Seoul, Korea," Journal of Soil and Groundwater Environment, vol. 25, no. 3, pp. 74-83, Sep. 2020. DOI: https://doi.org/10.7857/JSGE.2020.25.3.074
  5. T. Civas, F. F. Soulie, P. Gallinari, and S. Raudys, "Variable selection with neural networks," Neurocomputing, Volume 12.2-3, pp.223-248, 1996. https://doi.org/10.1016/0925-2312(95)00121-2
  6. R. May, G. Dandy, and H. Maier, "Review of input variable selection methods for artificial neural networks," Artificial neural networks-methodological advances and biomedical applications, 10:16004, pp.19-44, 2011.
  7. https://web.kma.go.kr/weather/forecast/timeseries.jsp
  8. https://www.airkorea.or.kr/index