Near real-time significant wave height prediction along the coastline of Queensland using advanced hybrid machine learning models
Article
Article Title | Near real-time significant wave height prediction along the coastline of Queensland using advanced hybrid machine learning models |
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ERA Journal ID | 44294 |
Article Category | Article |
Authors | Khosravi, K., Ali, M. and Heddam, S. |
Journal Title | International Journal of Environmental Science and Technology |
Number of Pages | 18 |
Year | 2024 |
Publisher | Springer |
Place of Publication | Iran |
ISSN | 1735-1472 |
1735-2630 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s13762-024-05944-7 |
Web Address (URL) | https://link.springer.com/article/10.1007/s13762-024-05944-7 |
Abstract | The accurate prediction of significant wave height is essential for coastal and offshore engineering, and is especially important for producing renewable ocean wave energy. However, Hs is traditionally predicted using empirical or numerical models, which lack accuracy, are computationally demanding, or require extensive datasets. Due to chaotic nature, it is very challenging for empirical or numerical models to precisely predict Hs. This study developed and tested several standalone machine learning models for Hs prediction and explored hybrid versions of these models based on additive regression to further improve model accuracy. Half-hourly Hs data along with common variables measured at ocean buoys were collected from four sations (i.e., Mooloolaba, Gladstone, Caloundra and Brisbane) along the coastline of Queensland, Australia and used to develop the ML models. The ML models were tested for their ability to accurately predict Hs at Mooloolaba station and were transferred to the three other stations to prove their spatial generalization capabilities. Overall, the results demonstrate that the ML models, and especially their hybrid versions, can accurately predict Hs at Mooloolaba as well as for other stations. Thus, the proposed models may serve as promising tools for improving ocean wave energy production. |
Keywords | Australia’s coasts; Hybrid algorithms; Machine learning; Signifcant wave height prediction |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 461199. Machine learning not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | University of Prince Edward Island, Canada |
UniSQ College | |
Al-Ayen University, Iraq | |
University 20 August 1955, Algeria |
https://research.usq.edu.au/item/z9460/near-real-time-significant-wave-height-prediction-along-the-coastline-of-queensland-using-advanced-hybrid-machine-learning-models
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