Forecasting Ambient Air So2 Concentrations Using Artificial Neural Networks
| dc.contributor.author | Sofuoğlu, Sait Cemil | |
| dc.contributor.author | Sofuoğlu, Aysun | |
| dc.contributor.author | Birgili, Savaş | |
| dc.contributor.author | Tayfur, Gökmen | |
| dc.coverage.doi | 10.1080/009083190881526 | |
| dc.date.accessioned | 2016-05-03T12:51:25Z | |
| dc.date.available | 2016-05-03T12:51:25Z | |
| dc.date.issued | 2006 | |
| dc.description.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. | en_US |
| dc.identifier.citation | 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 | en_US |
| dc.identifier.doi | 10.1080/009083190881526 | en_US |
| dc.identifier.doi | 10.1080/009083190881526 | |
| dc.identifier.issn | 1556-7249 | |
| dc.identifier.issn | 1556-7257 | |
| dc.identifier.scopus | 2-s2.0-33748508614 | |
| dc.identifier.uri | http://doi.org/10.1080/009083190881526 | |
| dc.identifier.uri | https://hdl.handle.net/11147/4592 | |
| dc.language.iso | en | en_US |
| dc.publisher | Taylor and Francis Ltd. | en_US |
| dc.relation.ispartof | Energy Sources, Part B: Economics, Planning and Policy | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | Air pollution | en_US |
| dc.subject | Artificial neural networks | en_US |
| dc.subject | Forecasting | en_US |
| dc.subject | Sulfur dioxide | en_US |
| dc.subject | Correlation coefficient | en_US |
| dc.title | Forecasting Ambient Air So2 Concentrations Using Artificial Neural Networks | en_US |
| dc.type | Article | en_US |
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| gdc.author.institutional | Sofuoğlu, Sait Cemil | |
| gdc.author.institutional | Sofuoğlu, Aysun | |
| gdc.author.institutional | Tayfur, Gökmen | |
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| gdc.coar.type | text::journal::journal article | |
| gdc.collaboration.industrial | false | |
| gdc.description.department | İzmir Institute of Technology. Chemical Engineering | en_US |
| gdc.description.department | İzmir Institute of Technology. Civil Engineering | en_US |
| gdc.description.endpage | 136 | en_US |
| gdc.description.issue | 2 | en_US |
| gdc.description.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
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| gdc.description.startpage | 127 | en_US |
| gdc.description.volume | 1 | en_US |
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| gdc.oaire.keywords | Correlation coefficient | |
| gdc.oaire.keywords | Artificial neural networks | |
| gdc.oaire.keywords | Sulfur dioxide | |
| gdc.oaire.keywords | Air pollution | |
| gdc.oaire.keywords | Forecasting | |
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