A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile

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datacite.alternateIdentifier.citationAtmospheric Environment, Vol. 42, N°35, 8331-8340, 2008es
datacite.alternateIdentifier.doi10.1016/j.atmosenv.2008.07.020es
datacite.creatorDíaz-Robles, Luis Alonso
datacite.creatorOrtega, J.C.
datacite.creatorFu, J.S.
datacite.creatorReed, G.D.
datacite.creatorChow, Judith C.
datacite.creatorWatson, J.G.
datacite.creatorMoncada-Herrera, J.A.
datacite.creatorDíaz-Robles, Luis Alonso
datacite.date2008
datacite.date.issued2012-02-23
datacite.subjectPoluciónes
datacite.subjectCalidad del airees
datacite.subjectInteligencia artificiales
datacite.subjectMaterial particuladoes
datacite.titleA hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chilees
dc.coverageTemucoes
dc.date.accessioned2012-02-24T02:33:12Z
dc.date.available2012-02-24T02:33:12Z
dc.description.abstractAir quality time series consists of complex linear and non-linear patterns and are difficult to forecast. Box-Jenkins Time Series (ARIMA) and multilinear regression (MLR) models have been applied to air quality forecasting in urban areas, but they have limited accuracy owing to their inability to predict extreme events. Artificial neural networks (ANN) can recognize non-linear patterns that include extremes. A novel hybrid model combining ARIMA and ANN to improve forecast accuracy for an area with limited air quality and meteorological data was applied to Temuco, Chile, where residential wood burning is a major pollution source during cold winters, using surface meteorological and PM10 measurements. Experimental results indicated that the hybrid model can be an effective tool to improve the PM10 forecasting accuracy obtained by either of the models used separately, and compared with a deterministic MLR. The hybrid model was able to capture 100% and 80% of alert and pre-emergency episodes, respectively. This approach demonstrates the potential to be applied to air quality forecasting in other cities and countries.es
dc.formatPDFes
dc.identifier.urihttps://repositoriodigital.uct.cl/handle/10925/678
dc.language.isoenes
dc.sourceAtmospheric Environmentes
oaire.resourceTypeArtículo de Revistaes
uct.carreraIngeniería Civil Ambientales
uct.catalogadorjmges
uct.comunidadIngenieríaes
uct.facultadFacultad de Ingenieríaes
uct.indizacionISIes
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