Ocean waves are widely estimated using physics-based computational models, which predict how energy is transferred from the wind, dissipated, and transferred spatially across the ocean. Machine learning methods offer an opportunity to predict these data with significantly reduced data input and computational power. This paper describes a novel surrogate model developed using the random forest method, which replicates the spatial nearshore wave data estimated by a Simulating WAves Nearshore (SWAN) numerical model. By incorporating in-situ buoy observations, outputs were found to match observations at a test location more closely than the corresponding SWAN model. Furthermore, the required computational time reduced by a factor of 100. This methodology can provide accurate spatial wave data in situations where computational power and transmission are limited, such as autonomous marine vehicles or during coastal and offshore operations in remote areas. This represents a significant supplementary service to existing physics-based wave models.

使用基于物理学的计算模型对海浪进行了广泛的估计，该模型预测了能量如何从风中转移，消散以及如何在整个海洋中进行空间转移。机器学习方法提供了机会，可以显着减少数据输入和计算能力来预测这些数据。本文介绍了一种使用随机森林方法开发的新型替代模型，该模型可复制通过模拟WAves近岸（SWAN）数值模型估算的空间近岸波数据。通过合并原位浮标观测，发现输出比相应的SWAN模型更接近于测试位置的观测。此外，所需的计算时间减少了100倍。这种方法可以在计算能力和传输受到限制的情况下提供准确的空间波数据，例如自动驾驶的航海车辆或在偏远地区的沿海和近海作业期间。这是对现有基于物理学的波模型的重要补充服务。