Journal of Environmental Science International (한국환경과학회지)

The Korean Environmental Sciences Society (한국환경과학회)
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A Study on the PM2.5 forcasting Method in Busan Using Deep Neural Network

DNN을 활용한 부산지역 초미세먼지 예보방안

  • Woo-Gon Do (Busan Metropolitan City Institute of Health and Environment) ;
  • Dong-Young Kim (Busan Metropolitan City Institute of Health and Environment) ;
  • Hee-Jin Song (Busan Metropolitan City Institute of Health and Environment) ;
  • Gab-Je Cho (Busan Metropolitan City Institute of Health and Environment)
  • 도우곤 (부산광역시 보건환경연구원) ;
  • 김동영 (부산광역시 보건환경연구원) ;
  • 송희진 (부산광역시 보건환경연구원) ;
  • 조갑제 (부산광역시 보건환경연구원)
  • Received : 2023.08.16
  • Accepted : 2023.08.23
  • Published : 2023.08.31
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Abstract

The purpose of this study is to improve the daily prediction results of PM2.5 from the air quality diagnosis and evaluation system operated by the Busan Institute of Health and Environment in real time. The air quality diagnosis and evaluation system is based on the photochemical numerical model, CMAQ (Community multiscale air quality modeling system), and includes a 3-day forecast at the end of the model's calculation. The photochemical numerical model basically has limitations because of the uncertainty of input data and simplification of physical and chemical processes. To overcome these limitations, this study applied DNN (Deep Neural Network), a deep learning technique, to the results of the numerical model. As a result of applying DNN, the r of the model was significantly improved. The r value for GFS (Global forecast system) and UM (Unified model) increased from 0.77 to 0.87 and 0.70 to 0.83, respectively. The RMSE (Root mean square error), which indicates the model's error rate, was also significantly improved (GFS: 5.01 to 6.52 ug/m3 , UM: 5.76 to 7.44 ug/m3 ). The prediction results for each concentration grade performed in the field also improved significantly (GFS: 74.4 to 80.1%, UM: 70.0 to 77.9%). In particular, it was confirmed that the improvement effect at the high concentration grade was excellent.

Keywords

References

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