Forecasting wind speed using a reinforcement learning hybrid ensemble model: a high-speed railways strong wind signal prediction study in Xinjiang, China

Liu, B., Pan, X., Yang, R. et al. (5 more authors) (2023) Forecasting wind speed using a reinforcement learning hybrid ensemble model: a high-speed railways strong wind signal prediction study in Xinjiang, China. Transportation Safety and Environment, 5 (4). ARTN tdac064. ISSN 2631-6765

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Item Type: Article
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© The Author(s) 2022. Published by Oxford University Press on behalf of Central South University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

Keywords: wind speed forecasting; high-speed railways; signal decomposition; reinforcement learning; ensemble model
Dates:
  • Published: 8 September 2023
  • Published (online): 21 December 2022
  • Accepted: 28 October 2022
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Civil Engineering (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 05 Jan 2024 11:23
Last Modified: 05 Jan 2024 11:23
Published Version: http://dx.doi.org/10.1093/tse/tdac064
Status: Published
Publisher: Oxford University Press (OUP)
Identification Number: 10.1093/tse/tdac064
Open Archives Initiative ID (OAI ID):

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