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Title: Forecasting ambient air SO2 concentrations using artificial neural networks
Authors: Sofuoğlu, Sait Cemil
Sofuoğlu, Aysun
Birgili, Savaş
Tayfur, Gökmen
Sofuoğlu, Sait Cemil
Sofuoğlu, Aysun
Tayfur, Gökmen
Izmir Institute of Technology. Chemical Engineering
Izmir Institute of Technology. Civil Engineering
Keywords: Air pollution
Artificial neural networks
Sulfur dioxide
Correlation coefficient
Issue Date: Jul-2006
Publisher: Taylor and Francis Ltd.
Source: Sofuoğlu, S. C., Sofuoğlu, A., Birgili, S., and Tayfur, G. (2006). Forecasting ambient air SO2 concentrations using artificial neural networks. Energy Sources, Part B: Economics, Planning and Policy, 1(2), 127-136. doi:10.1080/009083190881526
Abstract: An Artificial Neural Networks (ANNs) model is constructed to forecast SO 2 concentrations in Izmir air. The model uses meteorological variables (wind speed and temperature) and measured particulate matter concentrations as input variables. The correlation coefficient between observed and forecasted concentrations is 0.94 for the network that uses all three variables as input parameters. The root mean square error value of the model is 3.60 g/mt 3 . Considering the limited number of available input variables, model performances show that ANNs are a promising method of modeling to forecast ambient air SO 2 concentrations in Izmir.
ISSN: 1556-7249
Appears in Collections:Environmental Engineering / Çevre Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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