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

Year: 2026
Type: article
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.
DOI: 10.3390/jmse14171602
Institution: Daejin University
Language: English
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7204806274
FWCI:
Cites: 21
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Related to: 10