Improving Oil Spill Numerical Simulations through Bayesian Optimization

Accurate oil spill predictions are crucial for mitigating environmental and socioeconomic impacts. Numerical models, like MEDSLIK-II (De Dominicis et al., 2013), simulate oil advection, dispersion, and transformation, but their performance depends heavily on the configuration of physical parameters, often requiring labor-intensive manual tuning based on expert judgment.

To address this limitation, we integrate MEDSLIK-II with a Bayesian Optimization (BO) framework to systematically identify the optimal parameter configuration, ensuring simulations closely match observed spatiotemporal oil spill distributions. Our optimization focuses on horizontal diffusivity and drift factor parameters, using the Fraction Skill Score as the objective metric to maximize, thus reducing the overlap between simulations and observations.

The approach is validated on the 2021 Baniyas (Syria) oil spill, demonstrating improved accuracy, reduced biases and lower computational costs compared to the standalone numerical model.

By integrating BO with the MEDSLIK-II numerical model, our method enhances oil spill prediction capabilities and provides a transferable, physically consistent optimization framework applicable to a wide range of geophysical challenges.

This work is conducted within the framework of the iMagine European project, which leverages Artificial Intelligence, including AI-assisted image generation, to advance a series of use cases in marine and oceanographic science.

 

References

De Dominicis, M., Pinardi, N., Zodiatis, G., & Lardner, R. (2013). MEDSLIK-II, a Lagrangian marine surface oil spill model for short-term forecasting – Part 1: Theory. Geoscientific Model Development, 6, 1851–1869. https://doi.org/10.5194/gmd-6-1851-2013