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

•Probabilistic framework to assess the risk of ships.•Hybrid approach and multiple data sources.•Assessment of ship dynamic risk and static risk. 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 l...

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Veröffentlicht in:Reliability engineering & system safety 2021-12, Vol.216, p.107993, Article 107993
Hauptverfasser: Yu, Qing, Teixeira, Ângelo Palos, Liu, Kezhong, Rong, Hao, Guedes Soares, Carlos
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container_issue
container_start_page 107993
container_title Reliability engineering & system safety
container_volume 216
creator Yu, Qing
Teixeira, Ângelo Palos
Liu, Kezhong
Rong, Hao
Guedes Soares, Carlos
description •Probabilistic framework to assess the risk of ships.•Hybrid approach and multiple data sources.•Assessment of ship dynamic risk and static risk. 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.
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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. 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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. 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subjects Automatic identification system data
Bayesian analysis
Bayesian Networks
Coastal waters
Data sources
Empirical analysis
Evaluation
Evidential Reasoning
Inspection
Integrated approach
Maritime risk analysis
Mathematical models
Mitigation
Parameters
Port State Control inspection data
Reliability engineering
Risk analysis
Risk assessment
Risk factors
Rule-based approach
Ships
Static and dynamic ship risk
Traffic flow
Traffic management
title An integrated dynamic ship risk model based on Bayesian Networks and Evidential Reasoning
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