Predicting significant wave height with artificial neural networks in the South Atlantic Ocean: a hybrid approach
Author:
Funder
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Conselho Nacional de Pesquisas - CNPQ
Publisher
Springer Science and Business Media LLC
Subject
Oceanography
Link
https://link.springer.com/content/pdf/10.1007/s10236-023-01546-y.pdf
Reference35 articles.
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2. Browne M, Castelle B, Strauss D et al (2007) Near-shore swell estimation from a global wind-wave model: spectral process, linear, and artificial neural network models. Coast Eng 54(5):445–460. https://doi.org/10.1016/j.coastaleng.2006.11.007. http://www.sciencedirect.com/science/article/pii/S0378383906001840
3. Caires S, Sterl A (2005) 100-year return value estimates for ocean wind speed and significant wave height from the ERA-40 data. J Clim 18(7):1032–1048. https://doi.org/10.1175/JCLI-3312.1. https://journals.ametsoc.org/jcli/article-pdf/18/7/1032/3796666/jcli-3312_1.pdf
4. Campos R, Guedes Soares C (2016) Hybrid model to forecast significant wave heights: Proceedings of the 3rd International Conference on Maritime Technology and Engineering. MARTECH 2016, Lisbon, Portugal, 4-6 July 2016 pp 1027–1035. https://doi.org/10.1201/b21890-138
5. Campos RM, Soares CG (2016) Comparison and assessment of three wave hindcasts in the North Atlantic Ocean. Journal of Operational Oceanography 9(1):26–44. https://doi.org/10.1080/1755876X.2016.1200249
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