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Can artificial neural networks be used to predict the origin of ozone episodes?

2014Journal article Restricted access
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dc.contributor.authorFontes, Tânia
dc.contributor.authorSilva, Luís
dc.contributor.authorSilva, Márcia
dc.contributor.authorBarros, Nelson
dc.contributor.authorCarvalho, Ana Cristina
dc.date.accessioned2021-02-02T19:02:04Z
dc.date.available2021-02-02T19:02:04Z
dc.date.issued2014
dc.date.updated2021-01-25T17:26:03Z
dc.description.abstractTropospheric ozone is a secondary pollutant having a negative impact on health and environment. To control and minimize such impact the European Community established regulations to promote a clean air all over Europe. However, when an episode is related with natural mechanisms as Stratosphere–Troposphere Exchanges (STE), the benefits of an action plan to minimize precursor emissions are inefficient. Therefore, this work aims to develop a tool to identify the sources of ozone episodes in order to minimize misclassification and thus avoid the implementation of inappropriate air quality plans. For this purpose, an artificial neural network model – the Multilayer Perceptron – is used as a binary classifier of the source of an ozone episode. Long data series, between 2001 and 2010, considering the ozone precursors, 7 Be activity and meteorological conditions were used. With this model, 2–7% of a mean error was achieved, which is considered as a good generalization. Accuracy measures for imbalanced data are also discussed. The MCC values show a good performance of the model (0.65–0.92). Precision and F1-measure indicate that the model specifies a little better the rare class. Thus, the results demonstrate that such a tool can be used to help authorities in the management of ozone, namely when its thresholds are exceeded due natural causes, as the above mentioned STE. Therefore, the resources used to implement an action plan to minimize ozone precursors could be better managed avoiding the implementation of inappropriate measures.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1016/j.scitotenv.2014.04.077pt_PT
dc.identifier.issn0048-9697
dc.identifier.slugcv-prod-201406
dc.identifier.urihttp://hdl.handle.net/10284/9331
dc.language.isoengpt_PT
dc.publisherElsevierpt_PT
dc.subjectHuman healthpt_PT
dc.subjectOzonept_PT
dc.subjectStratospherept_PT
dc.subjectTropospherept_PT
dc.subjectClassificationpt_PT
dc.subjectArtificial neural networkpt_PT
dc.titleCan artificial neural networks be used to predict the origin of ozone episodes?pt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage207pt_PT
oaire.citation.startPage197pt_PT
oaire.citation.titleScience of the Total Environmentpt_PT
oaire.citation.volume488-489pt_PT
person.familyNameFontes
person.familyNameAugusto Cruz de Azevedo Barros
person.givenNameTânia
person.givenNameNelson
person.identifier.ciencia-id2919-FCDB-B684
person.identifier.orcid0000-0001-5183-5321
person.identifier.orcid0000-0002-2628-9880
person.identifier.ridD-2414-2013
person.identifier.scopus-author-id37074672900
rcaap.cv.cienciaid2919-FCDB-B684 | Nelson Augusto Cruz de Azevedo Barros
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication4ef465cf-6c8e-4002-b6a5-984085550b87
relation.isAuthorOfPublication3460e4e6-bb4b-4ed2-a2d2-b4a778db0e9e
relation.isAuthorOfPublication.latestForDiscovery4ef465cf-6c8e-4002-b6a5-984085550b87

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