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.

波浪高度的估算对于一些沿海工程应用而言至关重要。这项研究提出了一个嵌套网格数值模型，并将其效率与三种人工神经网络（ANN），极限学习机（ELM）和支持向量回归（SVR）的波高建模方法进行了比较。通过地表风​​数据对模型进行训练。结果表明，所有模型通常都提供声音预测。由于研究区域的测深法具有很高的可变性，因此采用具有不同Whitecapping系数的嵌套网格的实现是提高数值模型效率的合适方法。即使ELM模型略胜于其他模型，ML模型的性能也没有显着差异。