Jonas Wehrle , Christopher Jung , Marco Giometto , Andreas Christen , Dirk Schindler
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引用次数: 0
Abstract
This study proposes a machine learning (ML) framework generating spatially-distributed mean wind fields at a given height above ground within arbitrary urban canopy geometries. The framework is based on the Random Forest formulation and is trained using building resolving large-eddy simulations of flow over a range of realistic urban environments. The model maps up to 10 morphometric parameters, including three newly developed ones, to the mean wind over a considered horizontal plane. Predictions are computed from an ensemble of models. In independent evaluation areas, the application of the newly developed morphometric parameters increases the prediction accuracy on average by over 34 % with strengths in predicting main flow channels and areas of notably low wind speeds better than previously described morphometric parameters alone. ML-models, such as the one presented herein, are fast and efficient and are therefore suitable for operational use.
期刊介绍:
Urban Climate serves the scientific and decision making communities with the publication of research on theory, science and applications relevant to understanding urban climatic conditions and change in relation to their geography and to demographic, socioeconomic, institutional, technological and environmental dynamics and global change. Targeted towards both disciplinary and interdisciplinary audiences, this journal publishes original research papers, comprehensive review articles, book reviews, and short communications on topics including, but not limited to, the following:
Urban meteorology and climate[...]
Urban environmental pollution[...]
Adaptation to global change[...]
Urban economic and social issues[...]
Research Approaches[...]