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 ...
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.
