The Development of a Bayesian Network Framework with Model Validation for Maritime Accident Risk Factor Assessment

An integrative approach to maritime accident risk factor assessment in accordance with formal safety assessment is proposed, which exploits the multifaceted capabilities of Bayesian networks (BNs) by consolidation of modelling, verification, and validation. The methodology for probabilistic modellin...

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Main Authors:	Lea Vojković, Ana Kuzmanić Skelin, Djani Mohovic, Damir Zec
Format:	article
Language:	EN
Published:	MDPI AG 2021
Subjects:	Bayesian belief network Bayesian network validation risk factor assessment Bayesian inference ship grounding model Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Online Access:	https://doaj.org/article/cfd2c5a9f33f411289cc577c2f9ba41a
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spelling	oai:doaj.org-article:cfd2c5a9f33f411289cc577c2f9ba41a2021-11-25T16:39:34ZThe Development of a Bayesian Network Framework with Model Validation for Maritime Accident Risk Factor Assessment10.3390/app1122108662076-3417https://doaj.org/article/cfd2c5a9f33f411289cc577c2f9ba41a2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10866https://doaj.org/toc/2076-3417An integrative approach to maritime accident risk factor assessment in accordance with formal safety assessment is proposed, which exploits the multifaceted capabilities of Bayesian networks (BNs) by consolidation of modelling, verification, and validation. The methodology for probabilistic modelling with BNs is well known and its application to risk assessment is based on the model verified though sensitivity analysis only, while validation of the model is often omitted due to a lack of established evaluation measures applicable to scarce real-world data. For this reason, in this work, the modified Lyapunov divergence measure is proposed as a novel quantitative assessor that can be efficiently exploited on an individual accident scenario for contributing causal factor identification, and thus can serve as the measure for validation of the developed expert elicited BN. The proposed framework and its approach are showcased for maritime grounding of small passenger ships in the Adriatic, with the complete grounding model disclosed, quantitative validation performed, and its utilization for causal factor identification and risk factor ranking presented. The data from two real-world grounding cases demonstrate the explanatory capabilities of the developed approach.Lea VojkovićAna Kuzmanić SkelinDjani MohovicDamir ZecMDPI AGarticleBayesian belief networkBayesian network validationrisk factor assessmentBayesian inferenceship grounding modelTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10866, p 10866 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Bayesian belief network Bayesian network validation risk factor assessment Bayesian inference ship grounding model Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	Bayesian belief network Bayesian network validation risk factor assessment Bayesian inference ship grounding model Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Lea Vojković Ana Kuzmanić Skelin Djani Mohovic Damir Zec The Development of a Bayesian Network Framework with Model Validation for Maritime Accident Risk Factor Assessment
description	An integrative approach to maritime accident risk factor assessment in accordance with formal safety assessment is proposed, which exploits the multifaceted capabilities of Bayesian networks (BNs) by consolidation of modelling, verification, and validation. The methodology for probabilistic modelling with BNs is well known and its application to risk assessment is based on the model verified though sensitivity analysis only, while validation of the model is often omitted due to a lack of established evaluation measures applicable to scarce real-world data. For this reason, in this work, the modified Lyapunov divergence measure is proposed as a novel quantitative assessor that can be efficiently exploited on an individual accident scenario for contributing causal factor identification, and thus can serve as the measure for validation of the developed expert elicited BN. The proposed framework and its approach are showcased for maritime grounding of small passenger ships in the Adriatic, with the complete grounding model disclosed, quantitative validation performed, and its utilization for causal factor identification and risk factor ranking presented. The data from two real-world grounding cases demonstrate the explanatory capabilities of the developed approach.
format	article
author	Lea Vojković Ana Kuzmanić Skelin Djani Mohovic Damir Zec
author_facet	Lea Vojković Ana Kuzmanić Skelin Djani Mohovic Damir Zec
author_sort	Lea Vojković
title	The Development of a Bayesian Network Framework with Model Validation for Maritime Accident Risk Factor Assessment
title_short	The Development of a Bayesian Network Framework with Model Validation for Maritime Accident Risk Factor Assessment
title_full	The Development of a Bayesian Network Framework with Model Validation for Maritime Accident Risk Factor Assessment
title_fullStr	The Development of a Bayesian Network Framework with Model Validation for Maritime Accident Risk Factor Assessment
title_full_unstemmed	The Development of a Bayesian Network Framework with Model Validation for Maritime Accident Risk Factor Assessment
title_sort	development of a bayesian network framework with model validation for maritime accident risk factor assessment
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/cfd2c5a9f33f411289cc577c2f9ba41a
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The Development of a Bayesian Network Framework with Model Validation for Maritime Accident Risk Factor Assessment

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