Improving Oil Slick Trajectory Simulations with Bayesian Optimization
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arXiv
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| Format: | Preprint |
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2025
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| author | Accarino, Gabriele De Carlo, Marco M. Atake, Igor Elia, Donatello Dissanayake, Anusha L. Neves, Antonio Augusto Sepp Ibañez, Juan Peña Epicoco, Italo Nassisi, Paola Fiore, Sandro Coppini, Giovanni |
| author_facet | Accarino, Gabriele De Carlo, Marco M. Atake, Igor Elia, Donatello Dissanayake, Anusha L. Neves, Antonio Augusto Sepp Ibañez, Juan Peña Epicoco, Italo Nassisi, Paola Fiore, Sandro Coppini, Giovanni |
| contents | Accurate simulations of oil spill trajectories are essential for supporting practitioners' response and mitigating environmental and socioeconomic impacts. Numerical models, such as MEDSLIK-II, simulate advection, dispersion, and transformation processes of oil particles. However, simulations heavily rely on accurate parameter tuning, still based on expert knowledge and manual calibration. To overcome these limitations, we integrate the MEDSLIK-II numerical oil spill model with a Bayesian optimization framework to iteratively estimate the best physical parameter configuration that yields simulation closer to satellite observations of the slick. We focus on key parameters, such as horizontal diffusivity and drift factor, maximizing the Fraction Skill Score (FSS) as a measure of spatio-temporal overlap between simulated and observed oil distributions. We validate the framework for the Baniyas oil incident that occurred in Syria between August 23 and September 4, 2021, which released over 12,000 $m^3$ of oil. We show that, on average, the proposed approach systematically improves the FSS from 5.82% to 11.07% compared to control simulations initialized with default parameters. The optimization results in consistent improvement across multiple time steps, particularly during periods of increased drift variability, demonstrating the robustness of our method in dynamic environmental conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_02749 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improving Oil Slick Trajectory Simulations with Bayesian Optimization Accarino, Gabriele De Carlo, Marco M. Atake, Igor Elia, Donatello Dissanayake, Anusha L. Neves, Antonio Augusto Sepp Ibañez, Juan Peña Epicoco, Italo Nassisi, Paola Fiore, Sandro Coppini, Giovanni Atmospheric and Oceanic Physics Artificial Intelligence I.2; I.6; J.2; G.3 Accurate simulations of oil spill trajectories are essential for supporting practitioners' response and mitigating environmental and socioeconomic impacts. Numerical models, such as MEDSLIK-II, simulate advection, dispersion, and transformation processes of oil particles. However, simulations heavily rely on accurate parameter tuning, still based on expert knowledge and manual calibration. To overcome these limitations, we integrate the MEDSLIK-II numerical oil spill model with a Bayesian optimization framework to iteratively estimate the best physical parameter configuration that yields simulation closer to satellite observations of the slick. We focus on key parameters, such as horizontal diffusivity and drift factor, maximizing the Fraction Skill Score (FSS) as a measure of spatio-temporal overlap between simulated and observed oil distributions. We validate the framework for the Baniyas oil incident that occurred in Syria between August 23 and September 4, 2021, which released over 12,000 $m^3$ of oil. We show that, on average, the proposed approach systematically improves the FSS from 5.82% to 11.07% compared to control simulations initialized with default parameters. The optimization results in consistent improvement across multiple time steps, particularly during periods of increased drift variability, demonstrating the robustness of our method in dynamic environmental conditions. |
| title | Improving Oil Slick Trajectory Simulations with Bayesian Optimization |
| topic | Atmospheric and Oceanic Physics Artificial Intelligence I.2; I.6; J.2; G.3 |
| url | https://arxiv.org/abs/2503.02749 |