Thermal comfort study of urban waterfront spaces in cold regions: Waterfront skyline control based on thermal comfort objectives

• Published in: Building and environment 2024-05, Vol.256, p.111515, Article 111515
• Main Authors: Jiang, Jiayang, Pan, Wente, Zhang, Ruinan, Hong, Yang, Wang, Jixian
• Format: Article
• Language: English
• Subjects: Artificial neural network; Cold regions; Outdoor thermal comfort; Urban form index; Waterfront spaces
• ISSN: 0360-1323
• DOI: 10.1016/j.buildenv.2024.111515

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. •Deciduous trees and frozen river's effects on cold waterfront spaces' thermal comfort was proved.•The surrounding urban's influence on the cold waterfront spaces' thermal comfort was revealed.•The annual proportion of thermal comfort area was used as the cold waterfront spaces' evaluation.•The ANN model for predicting the cold regions' waterfront spaces thermal comfort was trained.•The urban design factors was optimized by GA to improve the thermal comfort of waterfront spaces.