An integrated dynamic ship risk model based on Bayesian Networks and Evidential Reasoning

Qing Yu, Teixeira, Ângelo Palos, Kezhong Liu, Hao Rong and Carlos Guedes Soares

Reliability Engineering and System Safety, 2021, vol. 216, issue C

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.

Keywords: Maritime risk analysis; Static and dynamic ship risk; Bayesian Networks; Evidential Reasoning; Rule-based approach; Port State Control inspection data; Automatic identification system data (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:216:y:2021:i:c:s0951832021005032

DOI: 10.1016/j.ress.2021.107993

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