Adaptation of ANFIS model to assess thermal comfort of an urban square in moderate and dry climate
Adaptation of ANFIS model to assess thermal comfort of an urban square in moderate and dry climate
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DOI：
10.1007/s00477-015-1116-3
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发表时间：
2016-04
影响因子：
4.2
通讯作者：
Shahab Kariminia;Shervin Motamedi;Shahaboddin Shamshirband;D. Petković;Chandrabhushan Roy;R. Hashim
中科院分区：
文献类型：
--
作者：
Shahab Kariminia;Shervin Motamedi;Shahaboddin Shamshirband;D. Petković;Chandrabhushan Roy;R. Hashim
开放的城市空间，如广场或广场，其吸引力取决于游客的热舒适度。在这方面，评估这些休憩用地的环境沿着游客的人口因素是很重要的。本研究采用自适应神经模糊推理系统（ANFIS）的软计算方法，在炎热和寒冷的天气条件下，在伊朗的公共广场的游客的热舒适性进行了调查。ANFIS过程中的变量选择，以检测影响个人的舒适感的主要变量。模型的训练和测试数据是通过在一年中的热季和冷季进行实地测量和调查收集的。我们使用了18个输入参数，代表人口和环境因素，计算游客的热感觉，舒适感，和4个常见的指标，即平均辐射温度（Tmrt），平均生理等效温度（PET），标准有效温度（SET）和预测平均投票（PMV）。结果表明，在所考察的因素中，空气温度（Ta）是最有影响力的参数和最好的预测精度为个人的舒适感在所研究的城市广场。结果表明，Tacan最好地预测了室外舒适性的共同指标，即PMV，PET，SET，热感觉，Tmrt和舒适感觉相比，其他参数的最小误差分别为1.94，18.87，13.67，0.91，7.80和0.34%。ANFIS方案的一些主要优点是它适用于优化和自适应方法，并且计算效率高。
Attractiveness of the open urban spaces, such as plazas or squares, depends on the visitor’s thermal comfort. In this respect, it is important to assess the environment of such open space along with the demographic factors of the visitors. This study used the soft-computing method of adaptive neuro fuzzy inference system (ANFIS) to investigate the thermal comfort of visitors at a public square in Iran during hot and cold weather conditions. The ANFIS process for variable selection was implemented in order to detect the predominant variables affecting the individual’s comfortable feeling. Model’s training and testing data were collected through the field measurement and survey during hot and cold times of the year. We used 18 input parameters, representative of demographic and environmental factors, to compute visitor’s thermal sensation, comfort feeling, and 4 common indices, namely the mean radiant temperature (Tmrt), mean physiological equivalent temperature (PET), standard effective temperature (SET) and predicted mean vote (PMV). The results indicated that among the examined factors, the air temperature (Ta) is the most influential parameter and best predictor of accuracy for the individual’s comfort feeling at the studied urban square. The results show that Tacan best predict the common indices of outdoor comfort, namely the PMV, PET, SET, thermal sensation, Tmrt, and comfortable felling compared to other parameters with the least error of 1.94, 18.87, 13.67, 0.91, 7.80, and 0.34 %, respectively. Some of the main advantages of the ANFIS scheme are that it is adaptable to the optimization and adaptive methods, and is computationally efficient.