A combined ocean and oil model for model-based adaptive monitoring

This paper presents a combined ocean and oil model for adaptive placement of sensors in the immediate aftermath of oilspills. A key feature of this model is the ability to correct its predictions of spill location using continual measurement feedback from a low number of deployed sensors. This allows for a model of relatively low complexity compared to existing models, which in turn enables fast predictions. The focus of this paper is upon the modelling aspects and in-particular the trade-off between complexity and numerical efficiency. The presented model contains relevant ocean, wind and wave dynamics for short-term spill predictions. The model is used to simulate the 2019 Grande America spill, with results compared to satellite imagery. The predictions show good agreement, even after several days from the initial incident. As a precursor to future work, results are also presented that demonstrate how sensor feedback mitigates the effects of model inaccuracy.