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Publications
Jiang, H.Y., Zhang, J.F., Wan, C.P., Zhang, M.Y. and Guedes Soares, C. (2024), A data-driven bayesian network model for risk influencing factors quantification based on global maritime accident database, Ocean and Coastal Management, Vol. 259, 107473
The maritime transportation system carries various risks, which may result in accidents and sometimes bring severe economic loss and fatalities. The historical maritime accident database contains valuable knowledge on accident causations. A deep understanding of how the risk influencing factors (RIFs) affect maritime accidents based on historical data helps prevent accidents from happening in the future. Using a large dataset of 55469 maritime accidents from 2002 to 2022, a Bayesian network model is formulated for studying how RIFs affect maritime accidents. The interdependencies among the RIFs are modelled using a Tree Augmented Network (TAN) with sensitivity analysis. Also, the Most Probable Explanations for each accident type are identified. The results indicate that older, smaller, non-convenient flagships in the North Atlantic zone have a higher probability of accidents. The important order of the crucial RIFs on accident types is location, ship type, ship age, gross tonnage, and deadweight tonnage. The effect of different RIFs on various kinds of maritime accidents is also examined. Ship type is the most important RIF for hull damage, fire or explosion, and contact accidents. Cargo ships are at the most significant risk of grounding while fishing ships exhibit the highest risk of hull damage, fire or explosion, and foundering. Age is the most significant RIF for foundering, but ship location is the most significant RIF for machinery damage, grounding, and collision accidents. Based on the above findings, recommendations for reducing maritime risk and enhancing sustainable ocean and coastal development and conservation are comprehensively discussed.
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