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How Do Different Locations, Floors and Aspects Influence Indoor Radon Concentrations? An Empirical Study Using Neural Networks for a University Campus in Northwestern Turkey
Indoor radon (222Rn) 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 222Rn levels were selected as a function of air temperature (Ta), air pressure (Pa), relative humidity (RH), Ta by RH interaction, local time, location, floor and aspect. Elevated levels of indoor 222Rn 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 222Rn levels as a function of location or floor level and aspect, respectively, in addition to Ta, Pa, RH, Ta by RH interaction and time.
How Do Different Locations, Floors and Aspects Influence Indoor Radon Concentrations? An Empirical Study Using Neural Networks for a University Campus in Northwestern Turkey
Indoor radon (222Rn) 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 222Rn levels were selected as a function of air temperature (Ta), air pressure (Pa), relative humidity (RH), Ta by RH interaction, local time, location, floor and aspect. Elevated levels of indoor 222Rn 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 222Rn levels as a function of location or floor level and aspect, respectively, in addition to Ta, Pa, RH, Ta by RH interaction and time.
How Do Different Locations, Floors and Aspects Influence Indoor Radon Concentrations? An Empirical Study Using Neural Networks for a University Campus in Northwestern Turkey
Atik, S. (author) / Yetis, H. (author) / Denizli, H. (author) / Evrendilek, F. (author)
Indoor and Built Environment ; 22 ; 650-658
2013-08-01
9 pages
Article (Journal)
Electronic Resource
English
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