Predicting oil spill diffusion through generative adversarial models

• 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. Oil spills remain a critical threat to marine ecosystems, especially in high-risk and densely trafficked areas such as ports. Traditional physics-based models for predicting oil dispersion, though grounded in fluid dynamics, are often constrained by high computational cost and limited suitability for real-time applications. To overcome these challenges, this work introduces a deep learning framework based on a Conditional Deep Convolutional Generative Adversarial Network (cDC-GAN) for fast and accurate prediction of oil spill diffusion in port environments. Key environmental variables (wind direction and intensity, coastline geometry, and time after release) are used as conditioning inputs, each represented as a separate image channel. The method has been validated with an oil spill dataset from the Augusta port in Italy, achieving an intersection-over-union (IoU) exceeding 0.9 and inference times below 30 milliseconds per diffusion sequence. Comparison with DiffusionLSTM models has been performed, showing the superiority of the proposed approach. The proposed model effectively captures complex spatial interactions between the oil slick and coastal boundaries, demonstrating strong potential as a real-time decision-support tool for environmental monitoring and emergency response operations. The proposed cDC-GAN framework provides a data-driven predictive model that can be integrated into autonomous marine vehicle control and navigation systems, enabling adaptive planning, real-time situational awareness, and decision-making during emergency interventions.