In waterfronts, thermal comfort is affected by climate and urban morphology. Existing studies on waterfront thermal comfort have mainly focused on warm and hot regions, focusing on the cooling effects of water and vegetation during the summer. The research methods are mostly questionnaire surveys and simulations, whereas the urban form and climate characteristics of cold regions are rarely considered, and the results of these studies are seldom applied to design practices. This study focused on the thermal comfort of waterfronts in cold regions. Computational Fluid Dynamics, Mean Radiant Temperature, and the Universal Thermal Climatic Index were used to examine the effects of tree–water features (winter river ice and tree defoliation) on thermal comfort in cold regions, and correlation analyses were combined to screen for relevant urban morphology factors for waterfront thermal comfort. Regression analyses were conducted to understand the influence of six factors, namely, building orientation, floor area ratio, open space ratio, building height to distance between river and building ratio, standard deviation of the first building row, and vegetation concentration, on waterfront thermal comfort. After training and comparing the four thermal comfort prediction models, a genetic algorithm combined with an artificial neural network was applied to optimize the design of the urban morphology of the six waterfront blocks. The optimization improved the waterfront thermal comfort performance by 22%, 7%, 77%, 106%, 3%, and 3%, respectively.

中文翻译：

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寒冷地区城市滨水空间热舒适性研究：基于热舒适目标的滨水天际线控制

在滨水区，热舒适度受到气候和城市形态的影响。现有的滨水区热舒适性研究主要集中在温暖和炎热地区，重点关注夏季水和植被的降温效果。研究方法多为问卷调查和模拟，很少考虑寒冷地区的城市形态和气候特征，研究结果也很少应用于设计实践。本研究主要研究寒冷地区滨水区的热舒适性。利用计算流体动力学、平均辐射温度和通用热气候指数考察树木-水特征（冬季河冰和树木落叶）对寒冷地区热舒适性的影响，并结合相关性分析筛选相关城市滨水热舒适度的形态因素。通过回归分析，了解建筑朝向、容积率、空地比、建筑高度与河道距离与建筑比、第一排建筑标准差、植被浓度等6个因素对滨水热力的影响。舒适。经过对四种热舒适度预测模型的训练和比较后，应用遗传算法结合人工神经网络对六个滨水街区的城市形态进行优化设计。优化后滨水热舒适性能分别提高了22%、7%、77%、106%、3%和3%。