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Uncertainty is given a more prominent role than in the current state of art in the" /> <title>(PDF) A framework for risk analysis of maritime transportation systems: A case study for oil spill from tankers in a ship–ship collision | Jakub Montewka and Floris Goerlandt - Academia.edu</title> <link rel="canonical" href="https://www.academia.edu/11480013/A_framework_for_risk_analysis_of_maritime_transportation_systems_A_case_study_for_oil_spill_from_tankers_in_a_ship_ship_collision" /> <script async src="https://www.googletagmanager.com/gtag/js?id=G-5VKX33P2DS"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-5VKX33P2DS', { cookie_domain: 'academia.edu', send_page_view: false, }); gtag('event', 'page_view', { 'controller': "single_work", 'action': "show", 'controller_action': 'single_work#show', 'logged_in': 'false', 'edge': 'unknown', // Send nil if there is no A/B test bucket, in case some records get logged // with missing data - that way we can distinguish between the two cases. // ab_test_bucket should be of the form <ab_test_name>:<bucket> 'ab_test_bucket': null, }) </script> <script> var $controller_name = 'single_work'; var $action_name = "show"; var $rails_env = 'production'; var $app_rev = '92477ec68c09d28ae4730a4143c926f074776319'; var $domain = 'academia.edu'; var $app_host = "academia.edu"; var $asset_host = "academia-assets.com"; var $start_time = new Date().getTime(); var $recaptcha_key = "6LdxlRMTAAAAADnu_zyLhLg0YF9uACwz78shpjJB"; var $recaptcha_invisible_key = "6Lf3KHUUAAAAACggoMpmGJdQDtiyrjVlvGJ6BbAj"; var $disableClientRecordHit = false; </script> <script> window.require = { config: function() { return function() {} } } </script> <script> window.Aedu = window.Aedu || {}; window.Aedu.hit_data = null; window.Aedu.serverRenderTime = new Date(1732775487000); window.Aedu.timeDifference = new Date().getTime() - 1732775487000; </script> <script type="application/ld+json">{"@context":"https://schema.org","@type":"ScholarlyArticle","abstract":"This paper proposes a framework for risk analysis of maritime transportation systems, where risk analysis is understood as a tool for argumentative decision support. Uncertainty is given a more prominent role than in the current state of art in the maritime transportation application area, and various tools are presented for analyzing uncertainty. A two-stage risk description is applied. In the first stage, Bayesian Network (BN) modeling is applied for probabilistic risk quantification. The model functions as a communication and argumentation tool, serving as an aid to thinking in a qualitative evidence and assumption effect assessment. The evidence assessment is used together with a sensitivity analysis to select alternative hypotheses for the risk quantification, while the assumption effect assessment is used to convey an argumentation beyond the model. Based on this, a deliberative uncertainty judgment is made in the second risk analysis stage, which is supplemented with a global strength of evidence assessment. The framework is applied to a case study of oil spill from tanker collisions, aimed at response capacity planning and ecological risk assessment. The BN-model is a proactive and transferable tool for assessing the occurrence of various spill sizes in a sea area. While the case study uses evidence specific to the Gulf of Finland, the model and risk analysis approach can be applied to other areas. Based on evaluation criteria and tests for the risk model and risk analysis, it is found that the model is a plausible representation of tanker collision oil spill risk.","author":[{"@context":"https://schema.org","@type":"Person","name":"Jakub Montewka"},{"@context":"https://schema.org","@type":"Person","name":"Floris Goerlandt"}],"contributor":[{"@context":"https://schema.org","@type":"Person","name":"Floris Goerlandt"}],"dateCreated":"2015-03-17","dateModified":"2015-03-17","datePublished":"2015-07-01","headline":"A framework for risk analysis of maritime transportation systems: A case study for oil spill from tankers in a ship–ship collision","inLanguage":"en","keywords":["Bayesian Networks","Risk Analysis","Oil Spills in the Marine Environment","Maritime Transportation","Maritime Accidents","Oil Tankers"],"locationCreated":null,"publication":"Safety Science","publisher":{"@context":"https://schema.org","@type":"Organization","name":null},"image":null,"thumbnailUrl":null,"url":"https://www.academia.edu/11480013/A_framework_for_risk_analysis_of_maritime_transportation_systems_A_case_study_for_oil_spill_from_tankers_in_a_ship_ship_collision","sourceOrganization":[{"@context":"https://schema.org","@type":"EducationalOrganization","name":"aalto-fi"},{"@context":"https://schema.org","@type":"EducationalOrganization","name":null}]}</script><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/single_work_page/loswp-102fa537001ba4d8dcd921ad9bd56c474abc201906ea4843e7e7efe9dfbf561d.css" /><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system/body-8d679e925718b5e8e4b18e9a4fab37f7eaa99e43386459376559080ac8f2856a.css" /><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system/button-3cea6e0ad4715ed965c49bfb15dedfc632787b32ff6d8c3a474182b231146ab7.css" /><link 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[{"id":37004435,"identifier":"Attachment_37004435","shouldShowBulkDownload":false}]; window.loswp.shouldDetectTimezone = true; window.loswp.shouldShowBulkDownload = true; window.loswp.showSignupCaptcha = false window.loswp.willEdgeCache = false; window.loswp.work = {"work":{"id":11480013,"created_at":"2015-03-17T03:04:00.006-07:00","from_world_paper_id":null,"updated_at":"2021-01-11T14:57:25.195-08:00","_data":{"abstract":"This paper proposes a framework for risk analysis of maritime transportation systems, where risk analysis is understood as a tool for argumentative decision support. Uncertainty is given a more prominent role than in the current state of art in the maritime transportation application area, and various tools are presented for analyzing uncertainty. A two-stage risk description is applied. In the first stage, Bayesian Network (BN) modeling is applied for probabilistic risk quantification. The model functions as a communication and argumentation tool, serving as an aid to thinking in a qualitative evidence and assumption effect assessment. The evidence assessment is used together with a sensitivity analysis to select alternative hypotheses for the risk quantification, while the assumption effect assessment is used to convey an argumentation beyond the model. Based on this, a deliberative uncertainty judgment is made in the second risk analysis stage, which is supplemented with a global strength of evidence assessment. The framework is applied to a case study of oil spill from tanker collisions, aimed at response capacity planning and ecological risk assessment. The BN-model is a proactive and transferable tool for assessing the occurrence of various spill sizes in a sea area. While the case study uses evidence specific to the Gulf of Finland, the model and risk analysis approach can be applied to other areas. Based on evaluation criteria and tests for the risk model and risk analysis, it is found that the model is a plausible representation of tanker collision oil spill risk.","publication_date":"2015,7,","publication_name":"Safety Science"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"A framework for risk analysis of maritime transportation systems: A case study for oil spill from tankers in a ship–ship collision","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [7811336,501011]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "full_page_mobile_sutd_modal"; window.loswp.useOptimizedScribd4genScript = false; window.loswp.appleClientId = 'edu.academia.applesignon';</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":37004435,"attachmentType":"pdf"}"><img alt="First page of “A framework for risk analysis of maritime transportation systems: A case study for oil spill from tankers in a ship–ship collision”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/37004435/mini_magick20190305-1872-yp66j6.png?1551812442" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/assets/single_work_splash/adobe.icon-574afd46eb6b03a77a153a647fb47e30546f9215c0ee6a25df597a779717f9ef.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">A framework for risk analysis of maritime transportation systems: A case study for oil spill from tankers in a ship–ship collision</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="7811336" href="https://independent.academia.edu/FlorisGoerlandt"><img alt="Profile image of Floris Goerlandt" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/7811336/4050910/4726161/s65_floris.goerlandt.jpg" />Floris Goerlandt</a><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="501011" href="https://aalto-fi.academia.edu/JakubMontewka"><img alt="Profile image of Jakub Montewka" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/501011/172741/201342/s65_jakub.montewka.jpg" />Jakub Montewka</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">2015, Safety Science</p></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">This paper proposes a framework for risk analysis of maritime transportation systems, where risk analysis is understood as a tool for argumentative decision support. Uncertainty is given a more prominent role than in the current state of art in the maritime transportation application area, and various tools are presented for analyzing uncertainty. A two-stage risk description is applied. In the first stage, Bayesian Network (BN) modeling is applied for probabilistic risk quantification. The model functions as a communication and argumentation tool, serving as an aid to thinking in a qualitative evidence and assumption effect assessment. The evidence assessment is used together with a sensitivity analysis to select alternative hypotheses for the risk quantification, while the assumption effect assessment is used to convey an argumentation beyond the model. Based on this, a deliberative uncertainty judgment is made in the second risk analysis stage, which is supplemented with a global strength of evidence assessment. The framework is applied to a case study of oil spill from tanker collisions, aimed at response capacity planning and ecological risk assessment. The BN-model is a proactive and transferable tool for assessing the occurrence of various spill sizes in a sea area. While the case study uses evidence specific to the Gulf of Finland, the model and risk analysis approach can be applied to other areas. Based on evaluation criteria and tests for the risk model and risk analysis, it is found that the model is a plausible representation of tanker collision oil spill risk.</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":37004435,"attachmentType":"pdf","workUrl":"https://www.academia.edu/11480013/A_framework_for_risk_analysis_of_maritime_transportation_systems_A_case_study_for_oil_spill_from_tankers_in_a_ship_ship_collision"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":37004435,"attachmentType":"pdf","workUrl":"https://www.academia.edu/11480013/A_framework_for_risk_analysis_of_maritime_transportation_systems_A_case_study_for_oil_spill_from_tankers_in_a_ship_ship_collision"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div></div><div data-auto_select="false" data-client_id="331998490334-rsn3chp12mbkiqhl6e7lu2q0mlbu0f1b" data-doc_id="37004435" data-landing_url="https://www.academia.edu/11480013/A_framework_for_risk_analysis_of_maritime_transportation_systems_A_case_study_for_oil_spill_from_tankers_in_a_ship_ship_collision" data-login_uri="https://www.academia.edu/registrations/google_one_tap" data-moment_callback="onGoogleOneTapEvent" id="g_id_onload"></div><div class="ds-top-related-works--grid-container"><div class="ds-related-content--container ds-top-related-works--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="0" data-entity-id="13233036" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/13233036/A_Bayesian_network_for_assessing_the_collision_induced_risk_of_an_oil_accident_in_the_Gulf_of_Finland">A Bayesian network for assessing the collision induced risk of an oil accident in the Gulf of Finland</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="32489265" href="https://slu-se.academia.edu/AnnukkaLehikoinen">Annukka Lehikoinen</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Environmental science & technology, 2015</p><p class="ds-related-work--abstract ds2-5-body-sm">The growth of maritime oil transportation in the Gulf of Finland (GoF), North-Eastern Baltic Sea, increases environmental risks by increasing the probability of oil accidents. 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href="https://www.academia.edu/54813497/Propagation_of_Uncertainty_In_a_Simulation_Based_Maritime_Risk_Assessment_Model_Utilizing_Bayesian_Simulation_Techniques"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="5" data-entity-id="44451025" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/44451025/Implementing_Bayesian_networks_for_ISO_31000_2018_based_maritime_oil_spill_risk_management_State_of_art_implementation_benefits_and_challenges_and_future_research_directions">Implementing Bayesian networks for ISO 31000:2018-based maritime oil spill risk management: State-of-art, implementation benefits and challenges, and future research directions</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="2337832" href="https://independent.academia.edu/P%C3%A4iviHaapasaari">Päivi Haapasaari</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Implementing Bayesian networks for ISO 31000:2018-based maritime oil spill risk management: State-of-art, implementation benefits and challenges, and future research directions, 2021</p><p class="ds-related-work--abstract ds2-5-body-sm">The risk of a large-scale oil spill remains significant in marine environments as international maritime transport continues to grow. The environmental as well as the socioeconomic impacts of a large-scale oil spill could be substantial. Oil spill models and modeling tools for Pollution Preparedness and Response (PPR) can support effective risk management. However, there is a lack of integrated approaches that consider oil spill risks comprehensively, learn from all information sources, and treat the system uncertainties in an explicit manner. Recently, the use of the international ISO 31000:2018 risk management framework has been suggested as a suitable basis for supporting oil spill PPR risk management. Bayesian networks (BNs) are graphical models that express uncertainty in a probabilistic form and can thus support decision-making processes when risks are complex and data are scarce. While BNs have increasingly been used for oil spill risk assessment (OSRA) for PPR, no link between the BNs literature and the ISO 31000:2018 framework has previously been made. This study explores how Bayesian risk models can be aligned with the ISO 31000:2018 framework by offering a flexible approach to integrate various sources of probabilistic knowledge. In order to gain insight in the current utilization of BNs for oil spill risk assessment and management (OSRA-BNs) for maritime oil spill preparedness and response, a literature review was performed. The review focused on articles presenting BN models that analyze the occurrence of oil spills, consequence mitigation in terms of offshore and shoreline oil spill response, and impacts of spills on the variables of interest. Based on the results, the study discusses the benefits of applying BNs to the ISO 31000:2018 framework as well as the challenges and further research needs.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Implementing Bayesian networks for ISO 31000:2018-based maritime oil spill risk management: State-of-art, implementation benefits and challenges, and future research directions","attachmentId":64873294,"attachmentType":"pdf","work_url":"https://www.academia.edu/44451025/Implementing_Bayesian_networks_for_ISO_31000_2018_based_maritime_oil_spill_risk_management_State_of_art_implementation_benefits_and_challenges_and_future_research_directions","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/44451025/Implementing_Bayesian_networks_for_ISO_31000_2018_based_maritime_oil_spill_risk_management_State_of_art_implementation_benefits_and_challenges_and_future_research_directions"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="6" data-entity-id="115446201" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/115446201/A_risk_assessment_model_with_systematical_uncertainty_treatment_for_container_shipping_operations">A risk assessment model with systematical uncertainty treatment for container shipping operations</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="295135100" href="https://independent.academia.edu/SonTungPhamNguyen">Son Tung Pham Nguyen</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Maritime Policy &amp; Management, 2020</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A risk assessment model with systematical uncertainty treatment for container shipping operations","attachmentId":111850897,"attachmentType":"pdf","work_url":"https://www.academia.edu/115446201/A_risk_assessment_model_with_systematical_uncertainty_treatment_for_container_shipping_operations","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/115446201/A_risk_assessment_model_with_systematical_uncertainty_treatment_for_container_shipping_operations"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="62007309" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/62007309/Weight_of_Evidence_Approach_to_Maritime_Accident_Risk_Assessment_Based_on_Bayesian_Network_Classifier">Weight of Evidence Approach to Maritime Accident Risk Assessment Based on Bayesian Network Classifier</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="24029668" href="https://uniri.academia.edu/DamirZec">Damir Zec</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Transactions on Maritime Science</p><p class="ds-related-work--abstract ds2-5-body-sm">Probabilistic maritime accident models based on Bayesian Networks are typically built upon the data available in accident records and the data obtained from human experts knowledge on accident. The drawback of such models is that they do not take explicitly into the account the knowledge on non-accidents as would be required in the probabilistic modelling of rare events. Consequently, these models have difficulties with delivering interpretation of influence of risk factors and providing sufficient confidence in the risk assessment scores. In this work, modelling and risk score interpretation, as two aspects of the probabilistic approach to complex maritime system risk assessment, are addressed. First, the maritime accident modelling is posed as a classification problem and the Bayesian network classifier that discriminates between accident and non-accident is developed which assesses state spaces of influence factors as the input features of the classifier. Maritime accident risk a...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Weight of Evidence Approach to Maritime Accident Risk Assessment Based on Bayesian Network Classifier","attachmentId":74888975,"attachmentType":"pdf","work_url":"https://www.academia.edu/62007309/Weight_of_Evidence_Approach_to_Maritime_Accident_Risk_Assessment_Based_on_Bayesian_Network_Classifier","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/62007309/Weight_of_Evidence_Approach_to_Maritime_Accident_Risk_Assessment_Based_on_Bayesian_Network_Classifier"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="16295033" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/16295033/A_cross_disciplinary_approach_to_minimising_the_risks_of_maritime_transport_in_the_Gulf_of_Finland">A cross-disciplinary approach to minimising the risks of maritime transport in the Gulf of Finland</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="35485560" href="https://helsinki.academia.edu/SakariKuikka">Sakari Kuikka</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="35480422" href="https://independent.academia.edu/UllaTapaninen">Ulla Tapaninen</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="35393603" href="https://aalto-fi.academia.edu/PenttiKujala">Pentti Kujala</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="32489265" href="https://slu-se.academia.edu/AnnukkaLehikoinen">Annukka Lehikoinen</a></div><p class="ds-related-work--metadata ds2-5-body-xs">World Review of Intermodal Transportation Research, 2009</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A cross-disciplinary approach to minimising the risks of maritime transport in the Gulf of Finland","attachmentId":42558836,"attachmentType":"pdf","work_url":"https://www.academia.edu/16295033/A_cross_disciplinary_approach_to_minimising_the_risks_of_maritime_transport_in_the_Gulf_of_Finland","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/16295033/A_cross_disciplinary_approach_to_minimising_the_risks_of_maritime_transport_in_the_Gulf_of_Finland"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="25492523" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/25492523/Bayesian_Network_Modelling_to_Enable_Actualisation_of_Decision_Making_Under_Uncertainty_in_Ship_Escape_Evacuation_and_Rescue_Operations">Bayesian Network Modelling to Enable Actualisation of Decision-Making Under Uncertainty in Ship Escape, Evacuation and Rescue Operations</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="49013279" href="https://ljmu.academia.edu/ASaajedi">A. Saajedi</a></div><p class="ds-related-work--abstract ds2-5-body-sm">The most desired output when making decisions under the realm of uncertainty on any safety-critical marine vessel and their systems is one that offers a powerful practical solution. With data and information typically being obtained incrementally, Bayesian network (BN) modelling is shown to realistically deal with the random uncertainties whilst at the same time making risk assessments easier to understand, build and check. Its inference processing relies on the sound Bayes' rule/theorem that permits the logic. Expanding one or more influencing nodal parameters with decision and utility node(s) also yields an influence diagram (ID). The feasibility of a BN modelling domain with ID capabilities is shown for a ship escape, evacuation and rescue operation scenario, as developed via a commercial computer tool. The results obtained give practical and cost-effective ranking order for risk control options in averting fatality. 1. INTRODUCTION If all the information that could be known about a maritime hazardous event/situation were obtainable for its risk assessment, then the results of such studies that are accurately carried out would not be subject to uncertainty. Instead, data and information is typically obtained incrementally. Thus, the inherent uncertainty can be due to imperfect understanding of the domain, incomplete knowledge of the state of the domain at the time where a given task is to be performed, randomness in the mechanisms governing the behaviour of the domain, or a combination of these. It is necessary then to model the assessment domain such that the probabilistic measure of each event becomes more reliable in light of the new information being received. In view of this, the domain that is represented can be put out in an intuitive visual format as a Bayesian network (BN) model. The BN reasoning system can be viewed as the generalisation of propositional logic and resolution theorem-proving that incorporates the treatment of uncertainty for the structure of the complex argument. Probability theory and Bayes' theorem ensures that inferences based on the network are sound. The semantics of typical BN models and added inference capabilities that are based on utility and decision theory can expand/transform such models into output influence diagrams that are highly intuitive in the decision-making process. Such diagrams aid the visibility of a large number of interacting issues and their effects on the decision. They can also offer the benefit of a robust practical solution that is required to achieve safety at an affordable cost. Hence, the final scheme of the BN can give a model in which reasoning is justified whilst it enables a powerful marine decision-support solution that is easy to use, flexible, and appropriate for the assessment task.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Bayesian Network Modelling to Enable Actualisation of Decision-Making Under Uncertainty in Ship Escape, Evacuation and Rescue Operations","attachmentId":45809833,"attachmentType":"pdf","work_url":"https://www.academia.edu/25492523/Bayesian_Network_Modelling_to_Enable_Actualisation_of_Decision_Making_Under_Uncertainty_in_Ship_Escape_Evacuation_and_Rescue_Operations","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/25492523/Bayesian_Network_Modelling_to_Enable_Actualisation_of_Decision_Making_Under_Uncertainty_in_Ship_Escape_Evacuation_and_Rescue_Operations"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":37004435,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":37004435,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_37004435" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. You can download the paper by clicking the button above.</p></div></div></div></div><div class="ds-sidebar--container js-work-sidebar"><div class="ds-related-content--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="0" data-entity-id="72581876" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/72581876/Enhanced_Decision_Making_through_Probabilistic_Shipwreck_Risk_Assessment_Focusing_on_the_Situation_in_Greece">Enhanced Decision Making through Probabilistic Shipwreck Risk Assessment: Focusing on the Situation in Greece</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="1133144" href="https://ntua.academia.edu/NikolaosVentikos">Nikolaos Ventikos</a></div><p class="ds-related-work--metadata 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data-author-id="24029668" href="https://uniri.academia.edu/DamirZec">Damir Zec</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Applied Sciences</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"The Development of a Bayesian Network Framework with Model Validation for Maritime Accident Risk Factor Assessment","attachmentId":74889026,"attachmentType":"pdf","work_url":"https://www.academia.edu/62007374/The_Development_of_a_Bayesian_Network_Framework_with_Model_Validation_for_Maritime_Accident_Risk_Factor_Assessment","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" 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