In this paper we analyse ocean wave crest statistics over different sized areas using data driven methods. We use second order numerical simulations to generate extreme crest data. We consider a simplistic Gumbel distribution fit as well as using a Random Forest Model to map the sea-state parameters to extreme crest values. Our simulations are compared with the existing distributions in the literature. We find that existing distributions perform well for more straightforward cases but that as more parameters are introduced the data science approach can capture features other methods cannot. Our approach also highlights the importance of different parameters such as steepness or length in the mean wave direction. We conclude that machine learning model is promising approach to predicting wave crest distributions in complex scenarios.

在本文中，我们使用数据驱动的方法分析了不同大小区域的海浪波峰统计数据。我们使用二阶数值模拟来生成极端波峰数据。我们考虑一个简单的 Gumbel 分布拟合以及使用随机森林模型将海况参数映射到极端峰值。我们的模拟与文献中的现有分布进行了比较。我们发现现有分布在更直接的情况下表现良好，但随着更多参数的引入，数据科学方法可以捕获其他方法无法捕获的特征。我们的方法还强调了不同参数的重要性，例如平均波浪方向的陡度或长度。我们得出结论，机器学习模型是预测复杂场景中波峰分布的有前途的方法。