The accurate prediction of the mean wave overtopping rate at breakwaters is vital for a safe design. Hence, providing a robust tool as a preliminary estimator can be useful for practitioners. Recently, soft computing tools such as artificial neural networks (ANN) have been developed as alternatives to traditional overtopping formulae. The goal of this paper is to assess the capabilities of two kernel-based methods, namely Gaussian process regression (GPR) and support vector regression for the prediction of mean wave overtopping rate at sloped breakwaters. An extensive dataset taken from the EurOtop database, including rubble mound structures with permeable core, straight slopes, without berm, and crown wall, was employed to develop the models. Different combinations of the important dimensionless parameters representing structural features and wave conditions were tested based on the sensitivity analysis for developing the models. The obtained results were compared with those of the ANN model and the existing empirical formulae. The modified Taylor diagram was used to compare the models graphically. The results showed the superiority of kernel-based models, especially the GPR model over the ANN model and empirical formulae. In addition, the optimal input combination was introduced based on accuracy and the number of input parameters criteria. Finally, the physical consistencies of developed models were investigated, the results of which demonstrated the reliability of kernel-based models in terms of delivering physics of overtopping phenomenon.

准确预测防波堤的平均波浪漫顶率对于安全设计至关重要。因此，提供一个强大的工具作为初步估计对从业者很有用。最近，诸如人工神经网络 (ANN) 之类的软计算工具已被开发为传统超顶公式的替代品。本文的目的是评估两种基于内核的方法的能力，即高斯过程回归 (GPR) 和支持向量回归，用于预测倾斜防波堤的平均波浪漫顶率。取自 EuroOtop 数据库的大量数据集，包括具有可渗透核心、直斜坡、无护堤和冠壁的碎石丘结构，用于开发模型。基于用于开发模型的灵敏度分析，测试了代表结构特征和波浪条件的重要无量纲参数的不同组合。将获得的结果与人工神经网络模型和现有经验公式的结果进行了比较。修改后的泰勒图用于以图形方式比较模型。结果表明基于核的模型，尤其是 GPR 模型优于 ANN 模型和经验公式。此外，基于准确性和输入参数数量标准引入了最佳输入组合。最后，研究了所开发模型的物理一致性，其结果证明了基于内核的模型在提供超载现象物理方面的可靠性。