Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 19, issue 20
Articles | Volume 19, issue 20
https://doi.org/10.5194/acp-19-12935-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-19-12935-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 20
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, Inyoung Park, Chul H. Song, Kyunghwa Lee, Jae W. Yun, Hong K. Kim, Moongu Jeon, Jiwon Lee, and Kyung M. Han

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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.
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