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Extrapolating low-occurrence strong wind speeds at pedestrian levels using artificial neural networks trained by a single turbulent dataset

Bibliographic Data

ID21232140
AuthorsYezhan Li (0000-0001-6829-0807, Tokyo Institute of Technology, corresponding author), Wei Wang (0000-0001-5806-2368, Tokyo Institute of Technology), T Okaze (0000-0001-7024-5511, Tokyo Institute of Technology), Naoki Ikegaya (0000-0002-7458-426X, Kyushu University)
Year2024
Volume115
Pages105837
Publication date2024-11-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueSustainable Cities and Society (JOURNAL)
Journal identifiersISSN: 2210-6707 • E-ISSN: 2210-6715
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.scs.2024.105837
OpenAlexW4402666262
LanguageEN
References cited32

Artificial neural network · Geography · Machine learning · Meteorology · Pedestrian · Transport engineering · Turbulence · Wind speed · Aerodynamics and Fluid Dynamics Research · Computer Science · Engineering · Environmental Science · Meteorological Phenomena and Simulations · Wind and Air Flow Studies · Artificial Intelligence

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