Abstract:Modeling urban microclimate accurately is challenging due to the high
surface heterogeneity of urban land cover and the vertical structure of
street morphology. Recent years have witnessed significant efforts in
numerical modeling and data collection of the urban environment.
Nonetheless, it is difficult for the physical-based models to fully
utilize the high-resolution data under the constraints of computing
resources. The advancement in machine learning techniques offers the
computational strength to handle … Show more
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