A simulation model for visitors' thermal comfort at urban public squares using non-probabilistic binary-linear classifier through soft-computing methodologies. (15th April 2016) - Kariminia, Shahab; Shamshirband, Shahaboddin; Hashim, Roslan; Saberi, Ahmadreza; Petković, Dalibor; Roy, Chandrabhushan; Motamedi, Shervin
Abstract: Sustaining outdoor life in cities is decreasing because of the recent rapid urbanisation without considering climate-responsive urban design concepts. Such inadvertent climatic modifications at the indoor level have imposed considerable demand on the urban energy resources. It is important to provide comfortable ambient climate at open urban squares. Researchers need to predict the comfortable conditions at such outdoor squares. The main objective of this study is predict the visitors' outdoor comfort indices by using a developed computational model termed as SVM-WAVELET ( Support Vector Machines combined with Discrete Wavelet Transform algorithm ). For data collection, the field study was conducted in downtown Isfahan, Iran (51°41′ E, 32°37′ N) with hot and arid summers. Based on different environmental elements, four separate locations were monitored across two public squares. Meteorological data were measured simultaneously by surveying the visitors' thermal sensations. According to the subjects' thermal feeling and their characteristics, their level of comfort was estimated. Further, the adapted computational model was used to estimate the visitors' thermal sensations in terms of thermal comfort indices. The SVM-WAVELET results indicate that R 2 value for input parameters, including Thermal Sensation, PMW (The predicted mean vote), PET (physiologically equivalent temperature), SET (standard effective temperature) and Tmrt were estimated at 0.482, 0.943, 0.988, 0.969 and 0.840, respectively. Highlights: To explore the visitors' thermal sensation at urban public squares. This article introduces findings of outdoor comfort prediction. The developed SVM-WAVELET soft-computing technique was used. SVM-WAVELET estimation results are more reliable and accurate.