Using machine learning to derive spatial wave data: A case study for a marine energy site
Jiaxin Chen, Ajit C. Pillai, Lars Johanning, Ian Ashton
Abstract
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.