Articles | Volume 15, issue 9
https://doi.org/10.5194/gmd-15-3797-2022
https://doi.org/10.5194/gmd-15-3797-2022
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

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-356', Anonymous Referee #1, 15 Dec 2021
  • RC2: 'Comment on gmd-2021-356', Anonymous Referee #2, 19 Dec 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Polina Shvedko on behalf of the Authors (24 Feb 2022)  Author's response
ED: Referee Nomination & Report Request started (27 Feb 2022) by Jinkyu Hong
RR by Anonymous Referee #2 (03 Mar 2022)
RR by Fearghal O'Donncha (14 Mar 2022)
ED: Publish subject to minor revisions (review by editor) (15 Mar 2022) by Jinkyu Hong
AR by Polina Shvedko on behalf of the Authors (21 Mar 2022)  Author's response
ED: Publish subject to minor revisions (review by editor) (24 Mar 2022) by Jinkyu Hong
AR by Jae-Bum Lee on behalf of the Authors (30 Mar 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (31 Mar 2022) by Jinkyu Hong
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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.