Significant wave height forecasting using long short-term memory neural network in Indonesian waters-Reference-Cited by-同舟云学术

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Significant wave height forecasting using long short-term memory neural network in Indonesian waters

Published:2022-03-04 Issue:2 Volume:8 Page:183-192
ISSN:2198-6444
Container-title:Journal of Ocean Engineering and Marine Energy
language:en
Short-container-title:J. Ocean Eng. Mar. Energy

Author:

Abdullah F. A. R.^ORCID,Ningsih N. S.,Al-Khan T. M.

Funder

Institut Teknologi Bandung Research Program 2021

Publisher

Springer Science and Business Media LLC

Subject

Ocean Engineering,Energy Engineering and Power Technology,Water Science and Technology,Renewable Energy, Sustainability and the Environment

Link

https://link.springer.com/content/pdf/10.1007/s40722-022-00224-3.pdf

Reference24 articles.

1. Akpınar A, van Vledder GP, Kömürcü Mİ, Özger M (2012) Evaluation of the numerical wave model (SWAN) for wave simulation in the Black Sea. Cont Shelf Res 50:80–99

2. Amrutha MM, Kumar VS, Sandhya KG, Nair TB, Rathod JL (2016) Wave hindcast studies using SWAN nested in WAVEWATCH III-comparison with measured nearshore buoy data off Karwar, eastern Arabian Sea. Ocean Eng 119:114–124

3. Berbić J, Ocvirk E, Carević D, Lončar G (2017) Application of neural networks and support vector machine for significant wave height prediction. Oceanologia 59(3):331–349

4. Gao S, Zhao P, Pan B, Li Y, Zhou M, Xu J, Zhong S, Shi Z (2018) A nowcasting model for the prediction of typhoon tracks based on a long short term memory neural network. Acta Oceanol Sin 37(5):8–12

5. Group, TW (1988) The WAM model—a third generation ocean wave prediction model. J Phys Oceanogr 18(12):1775–1810

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