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Discussion papers
https://doi.org/10.5194/acp-2019-268
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2019-268
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 29 Mar 2019

Submitted as: research article | 29 Mar 2019

Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Atmospheric Chemistry and Physics (ACP).

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

Hyun S. Kim1,*, Inyoung Park2,*, Chul H. Song1, Kyunghwa Lee1, Jae W. Yun2, Hong K. Kim2, Moongu Jeon2, and Jiwon Lee2 Hyun S. Kim et al.
  • 1School of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Korea
  • 2School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Korea
  • *Both authors are equally contributed to this work

Abstract. 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. The structural and learnable parameters of the newly developed system were optimized from iterative model trainings. Independent variables were obtained from ground-based observations over 2.3 years. The performance of the particulate matter (PM) prediction LSTM was then evaluated by comparisons with ground PM observations and with the PM concentrations predicted from two sets of 3-D chemistry-transport model (CTM) simulations (with and without data assimilation for initial conditions). The comparisons showed, in general, better performance with the LSTM than with the 3-D CTM simulations. For example, in terms of IOAs (index of agreements), the PM prediction IOAs were enhanced from 0.36–0.78 with the 3-D CTM simulations to 0.62–0.79 with the LSTM-based model. The deep LSTM-based PM prediction system developed at observation sites is expected to be further integrated with 3-D CTM-based prediction systems in the future. In addition to this, further possible applications of the deep LSTM-based system are discussed, together with some limitations of the current system.

Hyun S. Kim et al.
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Status: final response (author comments only)
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Hyun S. Kim et al.
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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-based system 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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