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Open AccessArticle

Estimation of Coastal Currents Using a Soft Computing Method: A Case Study in Galway Bay, Ireland

1
School of Marine Engineering and Technology, Sun Yat-sen University, Guangzhou 510275, China
2
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China
3
College of Science and Technology, Hebei Agricultural University, Cangzhou 061100, China
4
College of Engineering and Informatics, National University of Ireland Galway, H91 TK33 Galway, Ireland
*
Authors to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2019, 7(5), 157; https://doi.org/10.3390/jmse7050157
Received: 1 April 2019 / Revised: 14 May 2019 / Accepted: 14 May 2019 / Published: 20 May 2019
(This article belongs to the Special Issue Radar Technology for Coastal Areas and Open Sea Monitoring)
In order to obtain forward states of coastal currents, numerical models are a commonly used approach. However, the accurate definition of initial conditions, boundary conditions and other model parameters are challenging. In this paper, a novel application of a soft computing approach, random forests (RF), was adopted to estimate surface currents for three analysis points in Galway Bay, Ireland. Outputs from a numerical model and observations from a high frequency radar system were used as inputs to develop soft computing models. The input variable structure of soft computing models was examined in detail through sensitivity experiments. High correlation of surface currents between predictions from RF models and radar data indicated that the RF algorithm is a most promising means of generating satisfactory surface currents over a long prediction period. Furthermore, training dataset lengths were examined to investigate influences on prediction accuracy. The largest improvement for zonal and meridional surface velocity components over a 59-h forecasting period was 14% and 37% of root mean square error (RMSE) values separately. Results indicate that the combination of RF models with a numerical model can significantly improve forecasting accuracy for surface currents, especially for the meridional surface velocity component. View Full-Text
Keywords: coastal surface currents; soft computing; radar; sensitivity experiments; numerical model coastal surface currents; soft computing; radar; sensitivity experiments; numerical model
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MDPI and ACS Style

Ren, L.; Miao, J.; Li, Y.; Luo, X.; Li, J.; Hartnett, M. Estimation of Coastal Currents Using a Soft Computing Method: A Case Study in Galway Bay, Ireland. J. Mar. Sci. Eng. 2019, 7, 157.

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