Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/4594
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dc.contributor.authorSofuoğlu, Sait Cemil-
dc.date.accessioned2016-05-04T07:09:20Z
dc.date.available2016-05-04T07:09:20Z
dc.date.issued2008-06
dc.identifier.citationSofuoğlu, S. C. (2008). Application of artificial neural networks to predict prevalence of building-related symptoms in office buildings. Building and Environment, 43(6), 1121-1126. doi:10.1016/j.buildenv.2007.03.003en_US
dc.identifier.issn0360-1323
dc.identifier.issn0360-1323-
dc.identifier.urihttp://doi.org/10.1016/j.buildenv.2007.03.003
dc.identifier.urihttp://hdl.handle.net/11147/4594
dc.description.abstractArtificial neural networks (ANN) were constructed to predict prevalence of building-related symptoms (BRS) of office building occupants. Six indoor air pollutants and four indoor comfort variables were used as input variables to the networks. A symptom metric was used as the measure of BRS prevalence, and employed as the output variable. Pollutant concentration, comfort variable, and occupant symptom data were obtained from the Building Assessment and Survey Evaluation study conducted by the US Environmental Protection Agency, in which all were measured concurrently. Feed-forward networks that employ back-propagation algorithm with momentum term and variable learning rate were used in ANN modeling. Root mean square error and R2 value of the simple linear regression between observed and predicted output were used as performance measures. Among the constructed networks, the best prediction performance was observed in a one-hidden-layered network with an R2 value of 0.56 for the test set. All constructed networks except one showed a better performance than the multiple linear regression analysis.en_US
dc.language.isoenen_US
dc.publisherElsevier Ltd.en_US
dc.relation.ispartofBuilding and Environmenten_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial neural networksen_US
dc.subjectBuilding-related symptomsen_US
dc.subjectIndoor air qualityen_US
dc.subjectIndoor environmental qualityen_US
dc.subjectOffice buildingsen_US
dc.subjectEnvironmental protectionen_US
dc.titleApplication of artificial neural networks to predict prevalence of building-related symptoms in office buildingsen_US
dc.typeArticleen_US
dc.authoridTR59409en_US
dc.institutionauthorSofuoğlu, Sait Cemil-
dc.departmentİzmir Institute of Technology. Chemical Engineeringen_US
dc.identifier.volume43en_US
dc.identifier.issue6en_US
dc.identifier.startpage1121en_US
dc.identifier.endpage1126en_US
dc.identifier.wosWOS:000254216900017en_US
dc.identifier.scopus2-s2.0-38949194253en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1016/j.buildenv.2007.03.003-
dc.relation.doi10.1016/j.buildenv.2007.03.003en_US
dc.coverage.doi10.1016/j.buildenv.2007.03.003en_US
dc.identifier.wosqualityQ1-
dc.identifier.scopusqualityQ1-
item.languageiso639-1en-
item.fulltextWith Fulltext-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextopen-
item.openairetypeArticle-
crisitem.author.dept03.07. Department of Environmental Engineering-
Appears in Collections:Chemical Engineering / Kimya Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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