Development of an artificial neural network to predict benzene concentrations in a street canyon

dc.contributor.authorKarakitsios, S. P.en
dc.contributor.authorHadjidakis, I.en
dc.contributor.authorKassomenos, P. A.en
dc.contributor.authorPilidis, G. A.en
dc.date.accessioned2015-11-24T16:33:40Z
dc.date.available2015-11-24T16:33:40Z
dc.identifier.issn1018-4619-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/7693
dc.rightsDefault Licence-
dc.subjectbenzeneen
dc.subjectartificial neural networksen
dc.subjecttraffic flow patternsen
dc.subjectair-qualityen
dc.subjecttime-seriesen
dc.subjectmodelsen
dc.subjectareaen
dc.titleDevelopment of an artificial neural network to predict benzene concentrations in a street canyonen
heal.abstractNowadays, the prediction of atmospheric pollutant concentrations in street canyons' environment is of great importance. To achieve this, many kinds of modeling techniques were proposed. One of the most promising techniques is Artificial Neural Networks (ANNs). In this study, an ANN was developed to predict benzene concentrations in a heavily trafficted street canyon. It also evaluates the importance of the variables determining these concentrations. The training procedure was developed based on data collected by an annual measurement's campaign, performed in a specific street canyon. The data include benzene concentration, traffic flow and speed, vehicle's type distribution, wind speed and direction. The results from the simulations indicate that ANN is a promising technique for predicting benzene in an urban environment, and can be used for environmental management purposes.en
heal.accesscampus-
heal.fullTextAvailabilityTRUE-
heal.identifier.secondary<Go to ISI>://000237763100014-
heal.journalNameFresenius Environmental Bulletinen
heal.journalTypepeer reviewed-
heal.languageen-
heal.publicationDate2006-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Επιστημών και Τεχνολογιών. Τμήμα Βιολογικών Εφαρμογών και Τεχνολογιώνel
heal.typejournalArticle-
heal.type.elΆρθρο Περιοδικούel
heal.type.enJournal articleen

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