Extrapolating low-occurrence strong wind speeds at pedestrian levels using artificial neural networks trained by a single turbulent dataset
Bibliographic Data
| ID | 21232140 |
|---|---|
| Authors | Yezhan 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) |
| Year | 2024 |
| Volume | 115 |
| Pages | 105837 |
| Publication date | 2024-11-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sustainable Cities and Society (JOURNAL) |
| Journal identifiers | ISSN: 2210-6707 • E-ISSN: 2210-6715 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.scs.2024.105837 |
| OpenAlex | W4402666262 |
| Language | EN |
| References cited | 32 |
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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| Citation velocity | historical |
|---|---|
| Highly cited | No |