Prediction of significant wave height; comparison between nested grid numerical model, and machine learning models of artificial neural networks, extreme learning and support vector machines

Engineering Applications of Computational Fluid Mechanics

Shahaboddin Shamshirband¹  Kwok-wing Chau²  Amir Mosavi³  Narjes Nabipour⁴  Timon Rabczuk⁵ 

[1] Department for Management of Science and Technology Development, Ton Duc Thang University;Department of Civil and Environmental Engineering, Hong Kong Polytechnic University;Faculty of Civil Engineering, Technische Universität Dresden;Institute of Research and Development, Duy Tan University;Institute of Structural Mechanics, Bauhaus-Universität Weimar;

关键词: numerical modeling;    nested grid;    wind waves;    machine learning;    extreme learning machines;    deep learning;   

DOI  :  10.1080/19942060.2020.1773932

来源: DOAJ

【 摘 要 】

Estimation of wave height is essential for several coastal engineering applications. This study advances a nested grid numerical model and compare its efficiency with three machine learning (ML) methods of artificial neural networks (ANN), extreme learning machines (ELM) and support vector regression (SVR) for wave height modeling. The models are trained by surface wind data. The results demonstrate that all the models generally provide sound predictions. Due to the high level of variability in the bathymetry of the study area, implementation of the nested grid with different Whitecapping coefficient is a suitable approach to improve the efficiency of the numerical models. Performance on the ML models do not differ remarkably even though the ELM model slightly outperforms the other models.

【 授权许可】

Unknown