Citation

Zhang G, Thai VV. Safety Sci. 2016; 87: 53-62.

Copyright

(Copyright © 2016, Elsevier Publishing)

DOI

10.1016/j.ssci.2016.03.019

PMID

unavailable

Abstract

The Bayesian Network (BBN) has been a popular method for risk assessment especially for the modeling of rare accidents. It could make use of experts' domain knowledge when historical data were not enough to support the use of other statistical methods. In the maritime domain, the Bayesian Network has been widely used for risk prediction by modeling the causal relationship of shipping accidents where a lot of human and organizational factors are involved. Most of the models depend on experts' elicitation for model construction and parameterization. The involvement of experts' judgment brings uncertainty and biases. In contrast, data-driven BBN is considered more objective since it is learnt from empirical data. However, even though researchers started to explore the application of data-driven BBN in recent years, its application is still constrained due to the rare occurrence of maritime accidents and the incompatibility of accident databases. As a result, experts' knowledge continues to be an important source for modeling. Reducing the elicitation workload and facilitating the elicitation of individual conditional probability are the two most important tasks for BBN modeling with experts' knowledge. Different techniques that facilitate experts' elicitation process were reviewed in this paper. Some of these methods have been applied in the maritime risk model while new techniques should be developed and applied as well to address the uncertainty and improve accuracy of modeling shipping accidents.

Language: en