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Titolo:
Neural network modelling and prediction of hourly NOx and NO2 concentrations in urban air in London
Autore:
Gardner, MW; Dorling, SR;
Indirizzi:
Univ E Anglia, Sch Environm Sci, Norwich NR4 7TJ, Norfolk, England Univ E Anglia Norwich Norfolk England NR4 7TJ h NR4 7TJ, Norfolk, England
Titolo Testata:
ATMOSPHERIC ENVIRONMENT
fascicolo: 5, volume: 33, anno: 1999,
pagine: 709 - 719
SICI:
1352-2310(199902)33:5<709:NNMAPO>2.0.ZU;2-L
Fonte:
ISI
Lingua:
ENG
Soggetto:
TIME SCALES; OZONE;
Keywords:
air quality modelling; nitrogen oxides; primary pollutant; multilayer perceptron; artificial neural network;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Agriculture,Biology & Environmental Sciences
Physical, Chemical & Earth Sciences
Citazioni:
19
Recensione:
Indirizzi per estratti:
Indirizzo: Gardner, MW Univ E Anglia, Sch Environm Sci, Norwich NR4 7TJ, Norfolk, England Univ E Anglia Norwich Norfolk England NR4 7TJ orfolk, England
Citazione:
M.W. Gardner e S.R. Dorling, "Neural network modelling and prediction of hourly NOx and NO2 concentrations in urban air in London", ATMOS ENVIR, 33(5), 1999, pp. 709-719

Abstract

Multilayer perceptron (MLP) neural networks were trained to model hourly NOx and NO2 pollutant concentrations in Central London from basic hourly meteorological data. Results have shown that the models perform well when compared to previous attempts to model the same pollutants using regression based models. This work also illustrates that MLP neural networks are capable of resolving complex patterns of source emissions without any explicit external guidance. (C) 1999 Elsevier Science Ltd. All rights reserved.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 03/07/20 alle ore 01:29:13