1. Machine learning for ocean and coastal modeling
1. Machine learning for ocean and coastal modeling
Embedded Files
1) A Factorized Fourier Neural Operator Surrogate for Basin-Scale Tsunami Propagation
Model Zenodo Link: https://zenodo.org/records/19198928
Example tsunami-propagation comparison between the COMCOT reference simulation and the F-FNO emulator
The animation shows free-surface elevation η over the first 100 forecast frames
2) Unet++convLSTM based Ocean Wave Emulator using Deep Learning and Physics-Based Models
Model github link : https://github.com/fetchcast/Coastal-Wave-Emulator/
2. Delft3D-FM Flow and Wave Simulation for Ocean wave dynamics, Storm surge and coastal flooding
2. Delft3D-FM Flow and Wave Simulation for Ocean wave dynamics, Storm surge and coastal flooding
Suspended Sediment Concentration (mud) during a hurricane passage (without SWAN)
3. Directional design-wave projections under climate change
Korean coastal design waves are set from historical conditions. This study projects the directional 100-year significant wave height at 188 coastal stations to 2100, using SWAN driven by HadGEM3-RA regional climate output under SSP1-2.6 and SSP5-8.5. Comparing four extreme-value estimators shows that the projected change often depends on which tail model is assumed, and a storm-type attribution separates the directional signals that survive after tropical-cyclone-like storm windows are masked from those that do not.
Data and code: https://doi.org/10.5281/zenodo.22106408
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