An integrated 3D CNN-GRU deep learning method for short-term prediction of PM2.5 concentration in urban environment.

Faraji, Marjan; Nadi, Saeed; Ghaffarpasand, Omid; Homayoni, Saeid; Downey, Kay

Faraji, Marjan; Nadi, Saeed; Ghaffarpasand, Omid; Homayoni, Saeid; Downey, Kay.

Afiliação

Faraji M; Department of Geomatics Engineering, Faculty of Civil Engineering and Transportation, University of Isfahan, HezarJerib St., Isfahan 81746-73441, Iran. Electronic address: farajimarjan50@gmail.com.
Nadi S; Department of Civil and Environmental Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada. Electronic address: saeed.nadi@carleton.ca.
Ghaffarpasand O; School of Geography, Earth, and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK. Electronic address: o.ghaffarpasand@bham.ac.uk.
Homayoni S; Centre Eau Terre Environnement, Institut National de la Recherche Scientifique, Québec, QC G1K 9A9, Canada. Electronic address: Saeid.Homayouni@inrs.ca.
Downey K; School of Geography, Earth, and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK.

Sci Total Environ ; 834: 155324, 2022 Aug 15.

Article em En | MEDLINE | ID: mdl-35452742

Assuntos

Poluição do Ar; Aprendizado Profundo; Poluição do Ar/análise; Irã (Geográfico); Redes Neurais de Computação; Material Particulado/análise

Palavras-chave

Air pollution; Convolutional neural networks; Data science; Deep learning; Gated recurrent unit; Prediction

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Poluição do Ar / Aprendizado Profundo Tipo de estudo: Prognostic_studies / Risk_factors_studies País/Região como assunto: Asia Idioma: En Revista: Sci Total Environ Ano de publicação: 2022 Tipo de documento: Article

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