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Air quality forecasting using a hybrid autoregressive and nonlinear model

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Title Air quality forecasting using a hybrid autoregressive and nonlinear model
 
Creator Chelani, Asha B
Devotta, Sukumar
 
Subject Air Quality
 
Description The usual practices of air quality time-series forecasting are based on applying the models that deal with either the linear or nonlinear patterns. As the linear or nonlinear behavior of the time series is not known in advance, one applies the number of models and finally selects the one, which provides the most accurate results. The air pollutant concentration time series contain patterns that are not purely linear or nonlinear and applying either technique may give inadequate results. This study aims to develop a hybrid methodology that can deal with both the linear and nonlinear structure of the time series. The hybrid model is developed using the combination of autoregressive integrated moving average model,which deals with linear patterns and nonlinear dynamical model. To demonstrate the utility of the proposed technique,nitrogen dioxide concentration observed at a site in Delhi during 1999 to 2003 was utilized. The individual linear and nonlinear models were also applied in order to examine the performance of the hybrid model. The performance is
compared for one-step and multi-step ahead forecasts using the error statistics such as mean absolute percentage error and relative error. It is observed that hybrid model outperforms the individual linear and nonlinear models. The exploitation of unique features of linear and nonlinear models makes it a powerful technique to predict the air pollutant concentrations.
 
Publisher Elsevier
 
Date 2006
 
Type Article
PeerReviewed
 
Format application/pdf
 
Identifier http://neeri.csircentral.net/25/1/chelani_2006.pdf
Chelani, Asha B and Devotta, Sukumar (2006) Air quality forecasting using a hybrid autoregressive and nonlinear model. Atmospheric Environment, 40. pp. 1774-1780.
 
Relation http://neeri.csircentral.net/25/