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Article: Deep-AIR: A Hybrid CNN-LSTM Framework for Fine-Grained Air Pollution Estimation and Forecast in Metropolitan Cities

TitleDeep-AIR: A Hybrid CNN-LSTM Framework for Fine-Grained Air Pollution Estimation and Forecast in Metropolitan Cities
Authors
ZHANG, QHAN, YLi, VOKLam, JCK
Issue Date2022
Citation
IEEE Access, 2022, v. 10, p. 55818-55841 How to Cite?
DOI: http://dx.doi.org/10.1109/ACCESS.2022.3174853
Persistent Identifierhttp://hdl.handle.net/10722/320220
ISI Accession Number ID
WOS:000804624100001

 

DC FieldValueLanguage
dc.contributor.authorZHANG, Q-
dc.contributor.authorHAN, Y-
dc.contributor.authorLi, VOK-
dc.contributor.authorLam, JCK-
dc.date.accessioned2022-10-21T07:49:12Z-
dc.date.available2022-10-21T07:49:12Z-
dc.date.issued2022-
dc.identifier.citationIEEE Access, 2022, v. 10, p. 55818-55841-
dc.identifier.urihttp://hdl.handle.net/10722/320220-
dc.languageeng-
dc.relation.ispartofIEEE Access-
dc.titleDeep-AIR: A Hybrid CNN-LSTM Framework for Fine-Grained Air Pollution Estimation and Forecast in Metropolitan Cities-
dc.typeArticle-
dc.identifier.emailLi, VOK: vli@eee.hku.hk-
dc.identifier.emailLam, JCK: jcklam@eee.hku.hk-
dc.identifier.authorityLi, VOK=rp00150-
dc.identifier.authorityLam, JCK=rp00864-
dc.identifier.doi10.1109/ACCESS.2022.3174853-
dc.identifier.hkuros339691-
dc.identifier.volume10-
dc.identifier.spage55818-
dc.identifier.epage55841-
dc.identifier.isiWOS:000804624100001-

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