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Title: Application of artificial neural networks to predict prevalence of building-related symptoms in office buildings
Authors: Sofuoğlu, Sait Cemil
Keywords: Artificial neural networks
Building-related symptoms
Indoor air quality
Indoor environmental quality
Office buildings
Environmental protection
Publisher: Elsevier Ltd.
Source: Sofuoğlu, 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.2007.03.003
Abstract: Artificial 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.
ISSN: 0360-1323
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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