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.

Highlights

  • Reliable estimation of wave height in coastal waters can provide useful information for many different practical applications in coastal engineering, environmental monitoring, coastal protection, and marine transportation

  • Artificial intelligence techniques such as artificial neural network (ANN), adaptive neuro-fuzzy inference systems (ANFIS), support vector machine (SVM), and k-nearest neighbor (KNN) can be considered as nonlinear regression-based models which are black-box models to find a relationship between input and target variables without providing exact mathematical relationships and physics of the phenomena (Mosavi et al, 2018)

  • Sound prediction of significant wave height is considered as a key element for the design and construction of coastal protection structures, marine transportation, and offshore industry

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Summary

Introduction

Reliable estimation of wave height in coastal waters can provide useful information for many different practical applications in coastal engineering, environmental monitoring, coastal protection, and marine transportation. Regression-based models are mainly developed to find a statistical relationship between the target variable here means wave characteristics and some influencing variables such near-surface wind speed, wind blowing direction, mean sea level pressure among the others Artificial intelligence techniques such as artificial neural network (ANN), adaptive neuro-fuzzy inference systems (ANFIS), support vector machine (SVM), and k-nearest neighbor (KNN) can be considered as nonlinear regression-based models which are black-box models to find a relationship between input and target variables without providing exact mathematical relationships and physics of the phenomena (Mosavi et al, 2018).

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