Paper Info
Title
An Integrated Dynamic Ship Risk Model Based On Bayesian Networks And Evidential Reasoning
Abstract
The paper proposes a probabilistic framework for assessing the risk of ships based on a hybrid approach and multiple data sources. A Bayes-based network learning approach uses data from the New Inspection Regime of the Paris MoU on Port State Control to characterise the relationships among risk parameters and uses these parameters to evaluate the ship static risk. Other data sources are used to develop a Bayesian Network model to assess the dynamic risk of the ship. The data is aggregated by Bayesian Network and Evidential Reasoning approaches to evaluate the overall risk of ships in coastal waters. The objective of the study is to develop a model to assess the risk of an individual ship by considering its static risk profile and the geographical-dependant risk factors related to the characteristics of the maritime traffic flow and other local characteristics that influence the navigational risk of the ship. The results show that the integrated approach is able to assess the overall risk of a ship based on multiple data sources, providing empirical evidence of using multiple data sources in risk analysis applications. Moreover, the developed model identifies the most critical circumstances and the key impact factors in the study waters, which can support decisions on risk prevention and mitigation measures and local maritime traffic management.
Year
DOI
Venue
2021
10.1016/j.ress.2021.107993
RELIABILITY ENGINEERING & SYSTEM SAFETY
Keywords
DocType
Volume
Maritime risk analysis, Static and dynamic ship risk, Bayesian Networks, Evidential Reasoning, Rule-based approach, Port State Control inspection data, Automatic identification system data
Journal
216
ISSN
Citations 
PageRank 
0951-8320
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Qing Yu121.06
Ângelo Palos Teixeira201.35
Kezhong Liu331.08
H. Rong400.68
Carlos Guedes Soares58421.43