Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 9
Articles | Volume 15, issue 9
https://doi.org/10.5194/gmd-15-3797-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-3797-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 9
Development and technical paper
 | 
10 May 2022
Development and technical paper |  | 10 May 2022

Development of a deep neural network for predicting 6 h average PM2.5 concentrations up to 2 subsequent days using various training data

Jeong-Beom Lee, Jae-Bum Lee, Youn-Seo Koo, Hee-Yong Kwon, Min-Hyeok Choi, Hyun-Ju Park, and Dae-Gyun Lee

Supplement

https://doi.org/10.5194/gmd-15-3797-2022-supplement

Model code and software

Development of a deep neural network for predicting 6-hour average PM2.5 concentrations up to two subsequent days using various training data Jae-Bum Lee, Jeong-Beom Lee, Youn-Seo Koo, Hee-Yong Kwon, Min-Hyeok Choi, Hyun-Ju Park, and Dae-Gyun Lee https://doi.org/10.5281/zenodo.5652289

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Short summary
The predication of PM2.5 has been carried out using a numerical air quality model in South Korea. Despite recent progress of numerical air quality models, accurate prediction of PM2.5 is still challenging. In this study, we developed a data-based model using a deep neural network (DNN) to overcome the limitations of numerical air quality models. The results showed that the DNN model outperformed the CMAQ when it was trained by using observation and forecasting data from the numerical models.
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