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Analysis of factors affecting the severity of marine accidents using a data-driven Bayesian network by Yuhao Cao; Xinjian Wang; Yihang Wang; Shiqi Fan; Huanxin Wang; Zaili Yang; Zhengjiang Liu; Jin Wang; Runjie Shi is a Engineering article available to read on EtoBox.

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A data-driven Bayesian network model (BN) is used to analyse the relationship between the severity of marine accidents and relevant Accident Influential Factors (AIFs). Firstly, based on the marine accident investigation reports involving 1,294 ships from 2000 to 2019, the severity grades of marine accidents are classified, and a database of factors affecting the severity of marine accidents is established. Secondly, a Tree Augmented Naive Bayesian algorithm (TAN) is used to establish a data-driven BN model, and the established database of AIFs is analysed by data training and machine learning to reveal the influence of related factors on the severity of the accident and the mechanism of action. Finally, the sensitivity analysis and verification of the model are conducted. Through the analysis of the Most Probable Explanation (MPE), it explains the possible configurations in different scenarios and identifies the related potential risks. This study finds that “accident type”, “engine power”, “gross tonnage”, “ship type” and “location” are the five most important AIFs of three accident severity grades. “Capsizing/sinking”, “hull/machinery damage” and “collision” that are most likely

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Author

Yuhao Cao; Xinjian Wang; Yihang Wang; Shiqi Fan; Huanxin Wang; Zaili Yang; Zhengjiang Liu; Jin Wang; Runjie Shi

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Engineering (Physical Sciences)