Model-based understanding of uncertain observational data for oil spill tracking

Oil spill tracking is essential in oil spill clean-up. Usually, the oil spill tracking is treated by employing a mathematical oil spill model which describes the fate and transport behavior of an oil-spill. Before a model can predict where the spilled oil will go in the future, it must have a reasonably accurate understanding about what happened in the past. Typically, the input to the model such as wind, current, etc. is unreliable or sometimes not completely available so that interpolating the past behavior of an oil spill becomes extremely difficult. In this paper, we regard the oil spill tracking as a control problem in which we reduce the errors between the oil observations and the model outputs by iteratively adjusting the model inputs and cope with the uncertainty of the model inputs by using fuzzy logic techniques. Through the process, we can construct a plausible history of the oil spill that is consistent with our observations and can be effectively extrapolated into the future