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Taylor and Francis Group, Energy Sources Part B: Economics, Planning and Policy, 2(1), p. 127-136

DOI: 10.1080/009083190881526

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Forecasting Ambient Air SO2Concentrations Using Artificial Neural Networks

Journal article published in 2006 by Sait C. Sofuoglu ORCID, Aysun Sofuoglu, Savas Birgili, Gokmen Tayfur
This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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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.