Medical School, University of Cyprus, P.O.Box 20537, 1678, Nicosia, Cyprus.
Department of Computer Science and Biomedical Informatics, University of Thessaly, Papasiopoulou 2-4, 35131, Lamia, Greece.
Int J Biometeorol. 2022 Oct;66(10):1973-1984. doi: 10.1007/s00484-022-02333-y. Epub 2022 Jul 27.
Perception can influence individuals' behaviour and attitude affecting responses and compliance to precautionary measures. This study aims to investigate the performance of methods for thermal sensation and comfort prediction. Four machine learning algorithms (MLA), artificial neural networks, random forest (RF), support vector machines, and linear discriminant analysis were examined and compared to the physiologically equivalent temperature (PET). Data were collected in field surveys conducted in outdoor sites in Cyprus. The seven- and nine-point assessment scales of thermal sensation and a two-point scale of thermal comfort were considered. The models of MLA included meteorological and physiological features. The results indicate RF as the best MLA applied to the data. All MLA outperformed PET. For thermal sensation, the lowest prediction error (1.32 points) and the highest accuracy (30%) were found in the seven-point scale for the feature vector consisting of air temperature, relative humidity, wind speed, grey globe temperature, clothing insulation, activity, age, sex, and body mass index. The accuracy increased to 63.8% when considering prediction with at most one-point difference from the correct thermal sensation category. The best performed feature vector for thermal sensation also produced one of the best models for thermal comfort yielding an accuracy of 71% and an F-score of 0.81.
感知会影响个体的行为和态度，从而影响对预防措施的反应和遵守。本研究旨在调查热感觉和舒适预测方法的性能。研究考察并比较了四种机器学习算法（MLA），即人工神经网络、随机森林（RF）、支持向量机和线性判别分析，以及生理等效温度（PET）。数据是在塞浦路斯户外场所进行的实地调查中收集的。考虑了热感觉的七分制和九分制评估量表以及二分制的热舒适量表。MLA 的模型包括气象和生理特征。结果表明，RF 是应用于数据的最佳 MLA。所有 MLA 的表现均优于 PET。对于热感觉，在由空气温度、相对湿度、风速、灰球温度、服装热阻、活动、年龄、性别和体重指数组成的特征向量的七分制中，预测误差最小（1.32 点），准确率最高（30%）。当考虑预测与正确热感觉类别相差不超过一个点时，准确率提高到 63.8%。对于热感觉表现最佳的特征向量，也产生了热舒适表现最佳的模型之一，准确率为 71%，F 值为 0.81。
