Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 22
Articles | Volume 15, issue 22
https://doi.org/10.5194/gmd-15-8439-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-8439-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 22
Model description paper
 | 
21 Nov 2022
Model description paper |  | 21 Nov 2022

Development of an LSTM broadcasting deep-learning framework for regional air pollution forecast improvement

Haochen Sun, Jimmy C. H. Fung, Yiang Chen, Zhenning Li, Dehao Yuan, Wanying Chen, and Xingcheng Lu

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Latest update: 13 Dec 2024
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Short summary
This study developed a novel deep-learning layer, the broadcasting layer, to build an end-to-end LSTM-based deep-learning model for regional air pollution forecast. By combining the ground observation, WRF-CMAQ simulation, and the broadcasting LSTM deep-learning model, forecast accuracy has been significantly improved when compared to other methods. The broadcasting layer and its variants can also be applied in other research areas to supersede the traditional numerical interpolation methods.
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