Abstract Coastal structures are often designed to a maximum allowable wave overtopping discharge, hence accurate prediction of the amount of wave overtopping is an important issue. Both empirical formulae and neural networks are among the commonly used prediction tools. In this work, a new model for the prediction of mean wave overtopping discharge is presented using the innovative machine learning technique XGBoost. The selection of features to train the model on is carefully substantiated, including the redefinition of existing features to obtain a better model performance. Confidence intervals are derived by tuning hyperparameters and applying bootstrap resampling. The quality of the model is tested against four new physical model data sets, and a thorough quantitative comparison with existing machine learning methods and empirical overtopping formulae is presented. The XGBoost model generally outperforms other methods for the test data sets with normally incident waves. All data-driven methods show less accuracy on oblique wave data, presumably because these conditions are underrepresented in the training data. The performance of the XGBoost model is significantly improved by adding a randomly selected part of the new oblique wave cases to the training data. In the end, this new model is shown to reduce errors on all data used in this work with a factor of up to 6.5 compared to existing overtopping prediction methods.

中文翻译：

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使用先进的机器学习技术进行波浪超顶预测

摘要 沿海建筑物往往设计为最大允许浪溢流量，因此准确预测浪溢量是一个重要问题。经验公式和神经网络都是常用的预测工具。在这项工作中，使用创新的机器学习技术 XGBoost 提出了一种预测平均波越顶流量的新模型。训练模型的特征选择经过仔细证实，包括重新定义现有特征以获得更好的模型性能。置信区间是通过调整超参数和应用引导重采样得出的。模型的质量针对四个新的物理模型数据集进行了测试，并提供了与现有机器学习方法和经验超越公式的彻底定量比较。对于具有正常入射波的测试数据集，XGBoost 模型通常优于其他方法。所有数据驱动的方法在斜波数据上都显示出较低的准确性，大概是因为这些条件在训练数据中代表性不足。XGBoost 模型的性能通过将随机选择的部分新斜波案例添加到训练数据中得到显着提高。最后，与现有的超顶预测方法相比，这种新模型可以减少这项工作中使用的所有数据的误差，误差高达 6.5。所有数据驱动的方法在斜波数据上都显示出较低的准确性，大概是因为这些条件在训练数据中代表性不足。XGBoost 模型的性能通过将随机选择的部分新斜波案例添加到训练数据中得到显着提高。最后，与现有的超顶预测方法相比，这种新模型可以减少这项工作中使用的所有数据的误差，误差高达 6.5。所有数据驱动的方法在斜波数据上都显示出较低的准确性，大概是因为这些条件在训练数据中代表性不足。XGBoost 模型的性能通过将随机选择的部分新斜波案例添加到训练数据中得到显着提高。最后，与现有的超顶预测方法相比，这种新模型可以减少这项工作中使用的所有数据的误差，误差高达 6.5。