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ACP | Articles | Volume 19, issue 20
Atmos. Chem. Phys., 19, 12935–12951, 2019
https://doi.org/10.5194/acp-19-12935-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Chem. Phys., 19, 12935–12951, 2019
https://doi.org/10.5194/acp-19-12935-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 18 Oct 2019

Research article | 18 Oct 2019

Development of a daily PM10 and PM2.5 prediction system using a deep long short-term memory neural network model

Hyun S. Kim et al.
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Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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AR by Hyun Soo Kim on behalf of the Authors (07 Aug 2019)  Author's response    Manuscript
ED: Publish subject to minor revisions (review by editor) (27 Aug 2019) by David Topping
AR by Hyun Soo Kim on behalf of the Authors (28 Aug 2019)  Author's response    Manuscript
ED: Publish as is (16 Sep 2019) by David Topping
Publications Copernicus
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Short summary
In this study, a deep recurrent neural network system based on a long short-term memory (LSTM) model was developed for daily PM10 and PM2.5 predictions in South Korea. In general, the accuracies of the LSTM-based predictions were superior to the 3-D CTM-based predictions. Based on this, we concluded that the LSTM-based system could be applied to daily operational PM forecasts in South Korea. We expect that similar AI systems can be applied to the predictions of other atmospheric pollutants.
In this study, a deep recurrent neural network system based on a long short-term memory (LSTM)...
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