Liao, S.; Weng, J.; Zhang, Z.; Li, Z.; Li, F. Probabilistic Modeling of Maritime Accident Scenarios Leveraging Bayesian Network Techniques. J. Mar. Sci. Eng.2023, 11, 1513.
Liao, S.; Weng, J.; Zhang, Z.; Li, Z.; Li, F. Probabilistic Modeling of Maritime Accident Scenarios Leveraging Bayesian Network Techniques. J. Mar. Sci. Eng. 2023, 11, 1513.
Liao, S.; Weng, J.; Zhang, Z.; Li, Z.; Li, F. Probabilistic Modeling of Maritime Accident Scenarios Leveraging Bayesian Network Techniques. J. Mar. Sci. Eng.2023, 11, 1513.
Liao, S.; Weng, J.; Zhang, Z.; Li, Z.; Li, F. Probabilistic Modeling of Maritime Accident Scenarios Leveraging Bayesian Network Techniques. J. Mar. Sci. Eng. 2023, 11, 1513.
Abstract
This paper presents a scenario evolution model for maritime accidents using Bayesian networks (BN) to predict the most likely causes of specific types of maritime incidents. The BN nodes encompass accident type, life loss contingency, accident severity, the quarter and time period of the accident, and the type and gross tonnage of the ships involved. We analyzed 5,660 global maritime accidents from 2005 to 2020. Using Netica software, we constructed a Tree Augmented Network (TAN) model, accounting for interdependencies among risk influencing factors. We validated the results through sensitivity analysis and historical accident records. Forward causal inference and reverse diagnostic inference were then performed on each node variable to investigate the accident development trend and evolution process under predetermined conditions. The findings indicate that the model can effectively predict the likelihood of various accident scenarios under specific conditions, as well as the extrapolation of accident consequences. Forward causal reasoning reveals that general cargo ships with a gross tonnage of 1-18,500 t are most likely to experience collision, grounding, and stranding accidents in the first quarter. Reverse diagnostic reasoning indicates that during early morning hours, container ships, general cargo ships, and chemical ships with a tonnage of 1-18,500 t are less likely to involve life loss in the event of collision accidents.
Engineering, Safety, Risk, Reliability and Quality
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
