Box-Jenkins Time Series (ARIMA) and the multivariate linear models (MLM) have been important and popular linear tools in air quality forecasting during the past decade for urban areas. On the other hand, artificial neural networks (ANN) recently have been used successfully as a nonlinear tool in ...
Ortega-Bravo, J.C.
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Ramírez, M.
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Fu, J.S.
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Reed, G.D.
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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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A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile
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...
Díaz-Robles, Luis Alonso
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Ortega, J.C.
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Fu, J.S.
,
Reed, G.D.
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Chow, Judith C.
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Watson, J.G.
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Moncada-Herrera, J.A.
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Díaz-Robles, Luis Alonso
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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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A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile
The fine and ultra fine size of diesel particulate mater (DPM) are of great health concern and significantly contribute to the overall cancer risk. In addition, diesel particles may contribute a warming effect on the planet's climate. The composition of these particles is composed principally of ...
Díaz-Robles, Luis Alonso
,
Fu, J.S.
,
Reed, G.D.
,
DeLucía, A.J.
,
[Seasonal distribution and modeling of diesel particulate matter in the Southeast US]
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Seasonal distribution and modeling of diesel particulate matter in the Southeast US