Mid-long term oil spill forecast based on logistic regression modelling of met-ocean forcings.
Past major oil spill disasters, such as the Prestige or the Deepwater Horizon accidents, have shown that spilled oil may drift across the ocean for months before being controlled or reaching the coast. However, existing oil spill modelling systems can only provide short-term trajectory simulations, being limited by the typical met-ocean forecast time coverage. In this paper, we propose a methodology for mid-long term (1-6 months) probabilistic predictions of oil spill trajectories, based on a combination of data mining techniques, statistical pattern modelling and probabilistic Lagrangian simulations. Its main features are logistic regression modelling of wind and current patterns and a probabilistic trajectory map simulation. The proposed technique is applied to simulate the trajectory of drifting buoys deployed during the Prestige accident in the Bay of Biscay. The benefits of the proposed methodology with respect to existing oil spill statistical simulation techniques are analysed.
['Chiri|Helios|H|', 'Abascal|Ana Julia|AJ|', 'Castanedo|Sonia|S|', 'Medina|Raul|R|']
D003198:Computer Simulation D004784:Environmental Monitoring D005544:Forecasting D016015:Logistic Models D009792:Oceans and Seas D059392:Petroleum Pollution D014872:Water Movements D014874:Water Pollutants, Chemical D014919:Wind
Logistic regression Oil spill modelling Prestige accident Bay of Biscay Mid-long term forecast