Optimizing Medslik-II: Parametrization through a bayesian search algorithm applied at the Baniyas oil spill incident (Syria, 2021)

Abstract

Oil spill incidents can have a significative negative impact on coastal and marine ecosystems as well as human activities. Despite increased maritime safety, the European Maritime Safety Agency's Cleanseanet program detected a substantial increase in spills within the Mediterranean basin. The accurate prediction of the transport and transformation of the oil slick is a key aspect for assessing the impacts of the spill on coastal and marine areas. In this context, numerical oil spill modeling plays a crucial role in understanding unseen impacts and filling observational gaps. Such models are executed according to a set of physical simulation parameters, which are usually hand-picked, relying mostly on the modeler's expertise. Proper selection of such parameters is key for ensuring accurate results. This study proposes a novel technique integrating satellite observations, the Medslik-II oil spill model and Machine Learning for enhancing the oil spill results by optimizing the model parametrization. A Bayesian Optimization Framework was implemented to search for the optimal configuration through the parameter space. A real oil spill case, that occurred in the Baniyas area (Syria) in 2021, was used to validate the proposed approach. Results from early evaluation of such framework are promising and demonstrated that coupling physics- and data-driven techniques can lead to more precise risk assessment and planning for oil spill incidents.