黑匣子
白盒子
箱形模型
计算机科学
白色(突变)
环境科学
气象学
物理
工程类
机器学习
人工智能
化学
生物化学
基因
作者
Indira Adilkhanova,Jack Ngarambe,Geun Young Yun
标识
DOI:10.1016/j.rser.2022.112520
摘要
The urban heat island (UHI) phenomenon is a serious concern for urban planners and policymakers, requiring effective and efficient mitigation policies. To develop such policies, accurate and pre-emptive estimations of current and future UHI manifestations are vital elements that help determine efficient policies and mitigation techniques. There are two fundamental approaches for modelling overheating in an urban environment: white-box and black-box based methods. The first one is characterized by the easily interpretable working process, while the unclear working procedure defines the second one. The present study comprehensively reviews the commonly used white-box and black-box based approaches applied for UHI predictions, analyses the existing literature adopting these tools for UHI prediction, and discusses the effectiveness of fusing both methods at the design and operation stages of the urban area for effective prediction and mitigation of UHI effect. The literature analysis showed that the transparent working process and high prediction accuracy of the physical-based white-box models make them a popular and reliable tool for UHI evaluation. Nevertheless, some white-box based simulation tools are too complex and require a high level of expertise to operate, leading to potential inaccuracies in the obtained outcomes. Black-box models, in turn, despite their opaque working process, are more straightforward in use and require less computation time. The fusion of these two methods is a novel approach that may benefit both UHI prediction and mitigation at the design and operation stages, respectively. • Current use of white-box and black-box modelling of UHI is discussed. • Future research directions and areas of effective discourse are identified. • Fusion of white-box and black-box approaches for city design stages are discussed. • Coupling White-box modelling and machine-learning for city operations are proposed.
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