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

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

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dc.contributor.author Díaz-Robles, Luis Alonso
dc.contributor.author Ortega, J.C.
dc.contributor.author Fu, J.S.
dc.contributor.author Reed, G.D.
dc.contributor.author Chow, Judith C.
dc.contributor.author Watson, J.G.
dc.contributor.author Moncada-Herrera, J.A.
dc.contributor.author Díaz-Robles, Luis Alonso
dc.date 2008
dc.date.accessioned 2012-02-24T02:33:12Z
dc.date.available 2012-02-24T02:33:12Z
dc.date.issued 2012-02-23
dc.identifier.citation Atmospheric Environment, Vol. 42, N°35, 8331-8340, 2008
dc.identifier.uri https://hdl.handle.net/10925/678
dc.description.abstract Air 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.
dc.format PDF
dc.language.iso en
dc.source Atmospheric Environment
dc.subject Polución
dc.subject Calidad del aire
dc.subject Inteligencia artificial
dc.subject Material particulado
dc.title A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile
dc.type Artículo de Revista
uct.comunidad Ingeniería
uct.facultad Facultad de Ingeniería
uct.carrera Ingeniería Civil Ambiental
dc.identifier.doi 10.1016/j.atmosenv.2008.07.020
uct.catalogador jmg
dc.coverage Temuco
uct.indizacion ISI

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