A U-Net Based Surrogate Model for Rapid Prediction of Harbor Wave Fields Trained on SWAN Simulations

Abstract

Spectral wave models such as SWAN provide reliable harbor wave fields but require repeated, computationally expensive runs during preliminary design. This study develops a domain-knowledge-guided, U-Net-based surrogate model that rapidly approximates two-dimensional wave fields from SWAN simulations. The model takes water depth, incident significant wave height, incident wave direction, and a signed distance field (SDF) encoding structure geometry as inputs, and it combines a dynamic-resolution preprocessing scheme that preserves the terrain aspect ratio with a shadow-weighted loss function that emphasizes the sheltered zone behind breakwaters. Trained on 240 SWAN scenarios that combine 8 idealized geometries with 5 incident directions and 6 wave heights (about 15.7 million grid points), the model reproduced the SWAN significant wave height with a coefficient of determination of R2 = 0.8807 and a mean absolute error of 0.07 m over the whole domain under an untrained but within-range (interpolated) wave height condition, with comparable accuracy in the region of interest behind breakwaters (ROI R2 = 0.85). In a real sea application to Sokcho Harbor benchmarked against SWAN rather than field data, the model reproduced the offshore to harbor wave height pattern; along entrance and propagation-axis transects, it followed the SWAN profiles with high correlation (R ≈ 0.94–0.95), while overestimating the innermost calm zone waves by a non-negligible margin. The model is thus suited to rapid wave-field screening in preliminary harbor design.