Development of an LSTM broadcasting deep-learning framework for regional air pollution forecast improvement
Abstract 2.5 and O3 forecasts for the next 48 h were obtained using 3D chemical transport model simulation results and ground observation data as the inputs. The root mean square error associated with the proposed framework was 40 % and 20 % lower than those of the Weather Research and Forecasting–Community Multiscale Air Quality model and an offline combination of the deep-learning and spatial interpolation methods, respectively. The novel LSTM broadcasting framework can be extended for air pollution forecasting in other regions of interest.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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Development of an LSTM broadcasting deep-learning framework for regional air pollution forecast improvement ; volume:15 ; number:22 ; year:2022 ; pages:8439-8452 ; extent:14
Geoscientific model development ; 15, Heft 22 (2022), 8439-8452 (gesamt 14)
- Urheber
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Sun, Haochen
Fung, Jimmy C. H.
Chen, Yiang
Li, Zhenning
Yuan, Dehao
Chen, Wanying
Lu, Xingcheng
- DOI
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10.5194/gmd-15-8439-2022
- URN
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urn:nbn:de:101:1-2022112404304634171543
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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15.08.2025, 07:34 MESZ
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Beteiligte
- Sun, Haochen
- Fung, Jimmy C. H.
- Chen, Yiang
- Li, Zhenning
- Yuan, Dehao
- Chen, Wanying
- Lu, Xingcheng
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