Neural network modelling and prediction of hourly NOx and NO2 concentrations in urban air in London
- Author
- GARDNER, M. W1 ; DARLING, S. R1
[1] School of Environmental Sciences, University of East Anglia, Norwich, United Kingdom - Source
Atmospheric environment (1994). 1999, Vol 33, Num 5, pp 709-719 ; ref : 19 ref
- ISSN
- 1352-2310
- Scientific domain
- Environment; Climatology, meteorology; Pollution; Toxicology
- Publisher
- Elsevier Science, Oxford
- Publication country
- United Kingdom
- Document type
- Article
- Language
- English
- Keyword (fr)
- Azote Oxyde Azote dioxyde Condition météorologique Modélisation Perceptron multicouche Pollution air Qualité air Réseau neuronal Teneur Variation journalière Variation temporelle Zone urbaine NO2 NOx Angleterre Londres Europe Grande Bretagne Royaume Uni
- Keyword (en)
- Nitrogen Oxides Nitrogen dioxide Atmospheric condition Modeling Multilayer perceptrons Air pollution Air quality Neural network Content Daily variation Time variation Urban area England Europe Great Britain United Kingdom
- Keyword (es)
- Nitrógeno Óxido Nitrógeno dióxido Condición meteorológica Modelización Contaminación aire Calidad aire Red neuronal Proporción Variación diaria Variación temporal Zona urbana Inglaterra Europa Gran Bretaña Reino Unido
- Classification
- Pascal
- 001 Exact sciences and technology / 001D Applied sciences / 001D16 Pollution / 001D16C Atmospheric pollution / 001D16C02 Pollutants physicochemistry study: properties, effects, reactions, transport and distribution
- Discipline
- Pollution
- Origin
- Inist-CNRS
- Database
- PASCAL
- INIST identifier
- 1667452
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
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DOI
10.1016/S1352-2310(98)00230-1 
https://dx.doi.org/10.1016/S1352-2310(98)00230-1
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