8. Conclusion
In this paper, a Bayesian network model for the evaluation of accidental cargo oil outflow in ship–ship collisions involving a product tanker has been presented. The main focus of the paper is the presented framework for the construction of this model and assessment of the underlying uncertainties and biases in line with the intended adopted risk perspective in risk assessment of maritime transportation.
The probabilistic oil outflow model integrates a damage extent model conditional to impact scenarios with a model for evaluating the oil outflow based on an estimated tank arrangement. Based on a large set of damage cases in a set of representative product tanker designs, a network linking ship design variables, damage scenarios and oil outflow is constructed using a Bayesian learning algorithm. The impact scenario model is subsequently linked to the damage extent variables.
The model provides a platform to assess the uncertainty about the possible oil outflows in maritime traffic scenarios when only very limited data regarding the ship design is available, as is typical in risk assessment of maritime transportation. It also enables insight in the probabilistic nature of possible oil outflows conditional to the impact conditions, which has been illustrated in two example accident scenarios.