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

Shahaboddin Shamshirband, Amir Mosavi, Timon Rabczuk, Narjes Nabipour, Kwok wing Chau

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)805-817
Number of pages13
JournalEngineering Applications of Computational Fluid Mechanics
Volume14
Issue number1
DOIs
Publication statusPublished - 1 Jan 2020

Keywords

  • deep learning
  • extreme learning machines
  • machine learning
  • nested grid
  • Numerical modeling
  • wind waves

ASJC Scopus subject areas

  • Computer Science(all)
  • Modelling and Simulation

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