How do different locations, floors and aspects influence indoor radon concentrations? An empirical study using neural networks for a university campus in Northwestern Turkey

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dc.authorid0000-0003-1099-4363en_US
dc.authorid0000-0001-9423-0445en_US
dc.authorid0000-0003-1570-0344en_US
dc.contributor.authorAtik, Şeyma Yılmaz
dc.contributor.authorYetiş, Hakan
dc.contributor.authorDenizli, Haluk
dc.contributor.authorEvrendilek, Fatih
dc.date.accessioned2021-06-23T19:34:23Z
dc.date.available2021-06-23T19:34:23Z
dc.date.issued2013
dc.departmentBAİBÜ, Fen Edebiyat Fakültesi, Fizik Bölümüen_US
dc.description.abstractIndoor radon (Rn-222) concentrations were measured at a 10-min interval during October 2011 and January 2012. The monitoring followed a randomised and repeated pattern of experimental design, and was carried out at six faculty buildings of the Abant Izzet Baysal University, on five floor levels and two aspect directions (south vs. north) using an AlphaGUARD P30 Radon Monitor. The University campus area located in northwestern Turkey is near the North Anatolian Fault, a major active right lateral-moving strike-slip fault which runs along the transform boundary between the Eurasian Plate and the Anatolian Plate. Best artificial neural networks (ANNs) emulating indoor Rn-222 levels were selected as a function of air temperature (T-a), air pressure (P-a), relative humidity (RH), T-a by RH interaction, local time, location, floor and aspect. Elevated levels of indoor Rn-222 concentrations were measured at the south-facing offices and on the first floor levels of the building. Lower concentrations were found on the upper floor levels. Out of 27 ANNs, GFF-1-B-L and MLP-2-B-L performed best and could be contributing to the 35.6% and 87.2% of variations in spatio-temporal dynamics of indoor Rn-222 levels as a function of location or floor level and aspect, respectively, in addition to T-a, P-a, RH, T-a by RH interaction and time.en_US
dc.identifier.doi10.1177/1420326X12452284
dc.identifier.endpage658en_US
dc.identifier.issn1420-326X
dc.identifier.issn1423-0070
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-84881167031en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage650en_US
dc.identifier.urihttps://doi.org/10.1177/1420326X12452284
dc.identifier.urihttps://hdl.handle.net/20.500.12491/7491
dc.identifier.volume22en_US
dc.identifier.wosWOS:000322590200007en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorAtik, Şeyma Yılmaz
dc.institutionauthorDenizli, Haluk
dc.institutionauthorYetiş, Hakan
dc.institutionauthorEvrendilek, Fatih
dc.language.isoenen_US
dc.publisherSage Publications Ltden_US
dc.relation.ispartofIndoor And Built Environmenten_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectIndoor Radonen_US
dc.subjectAbant Izzet Baysal Universityen_US
dc.subjectNeural Networksen_US
dc.subjectSpatio-temporal Dynamicsen_US
dc.subjectModellingen_US
dc.titleHow do different locations, floors and aspects influence indoor radon concentrations? An empirical study using neural networks for a university campus in Northwestern Turkeyen_US
dc.typeArticleen_US

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