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Predicting oil spill diffusion through generative adversarial models
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DOI：10.1016/j.conengprac.2026.106977.png)
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Abstract
En 中文
• Fast and efficient oil spill diffusion prediction via cDC-GAN. • Integration of exogenous variables and coastal geometry to improve physical realism. • Prediction at arbitrary and independent timesteps. • Weighted binary cross-entropy loss to improve oil localization accuracy.
Keywords:
Marine systems
Oil spill diffusion
Conditional generative models
Environmental forecasting
Deep learning
Harbour areas
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