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A Bayesian Network Risk Model for Oil Spill Response Effectiveness in the Canadian Arctic (OSRECA)

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dc.contributor.authorAl Sharkawi, Talah
dc.contributor.copyright-releaseNot Applicableen_US
dc.contributor.degreeMaster of Applied Scienceen_US
dc.contributor.departmentDepartment of Industrial Engineeringen_US
dc.contributor.ethics-approvalReceiveden_US
dc.contributor.external-examinern/aen_US
dc.contributor.graduate-coordinatorDr. John Blakeen_US
dc.contributor.manuscriptsNot Applicableen_US
dc.contributor.thesis-readerDr. Ron Peloten_US
dc.contributor.thesis-readerDr. Hamid Afsharien_US
dc.contributor.thesis-readerDr. Haibo Niuen_US
dc.contributor.thesis-supervisorDr. Floris Goerlandten_US
dc.date.accessioned2022-12-19T18:23:27Z
dc.date.available2022-12-19T18:23:27Z
dc.date.defence2022-12-09
dc.date.issued2022-12-16
dc.descriptionThroughout the years, there has been an increase in various marine-related activities in the Arctic due to globalization. These include shipping activities, tourism, fisheries, research, mining, and offshore oil drilling. Such actions can lead to potential oil spills, the risk of which has been an increasing concern. When focusing on potential oil spills from shipping activities alone, they can have serious negative consequences to marine ecosystems, lead to important economic costs, and have widespread socio-economic, cultural and health impacts. Therefore, determining the efficiency of oil spill responses will help mitigate some of these negative consequences. To do this, a sub-model needs to be created as an analysis support where different spill types, clean-up technologies, human and environmental conditions are considered. By creating a model, one can tell what a system is doing under certain conditions and the factors and relationships that bring about this behaviour.  As such, developing a model for emergency response planning for oil spill incidents has a lot of complexity and uncertainty as there are various variables needed to be considered. Some variables include oil spill location, oil spill incidents, and oil spill size. A Bayesian Network Model is used to aid in understanding the effectiveness of oil spill responses for various scenarios in the Canadian Arctic. While the proposed model can be used as a basis for exploring response effectiveness, adequate attention to the strength of evidence on which the model is built is required. Hence, a strength of evidence, sensitivity analysis, and criticality matrix supplements the risk model, to provide information on the sensitivity of the effectiveness of the sub-models and the evidence on which the model is based. This thesis has aimed to generate a Bayesian Network model to provide insights in oil spill response processes, focused on the effectiveness of different response operations in selected conditions. After the OSRECA model was developed using an iterative process, multiple oil spill scenarios have been applied to give insights in the effectiveness of using specified oil spill response types in the Canadian Arctic.  Ten unique sub-models were created for the three main response types: Mechanical Recovery, Chemical Dispersant, and In-Situ Burning. The OSRECA BN model includes a total of 242 variables, with over 700 states, providing a high-level yet comprehensive view on the response system and its effectiveness under a range of possible contextual scenarios. Based on the analysis results; the model enables reasoning about spill response effectiveness in a consistent way. However, the available evidence underlying the model construction and parameterization is not strong enough to give very firm answers about the response effectiveness. Nevertheless, the model can be used to obtain high-level insights in the overall oil spill response effectiveness in the Canadian Arctic, and to discern trends and patterns. en_US
dc.description.abstractA Bayesian Network Model is used to aid in understanding the effectiveness of oil spill responses for various scenarios in the Canadian Arctic. While the proposed model can be used as a basis for exploring response effectiveness, adequate attention to the strength of evidence on which the model is built is required. Hence, a strength of evidence, sensitivity analysis, and criticality matrix supplements the risk model, to provide information on the sensitivity of the effectiveness of the sub-models and the evidence on which the model is based. This thesis aimed to generate a Bayesian Network model to provide insights in oil spill response processes, focused on the effectiveness of different response operations in selected conditions. Ten unique sub-models were created for the three main response types: Mechanical Recovery, Chemical Dispersant, and In-Situ Burningen_US
dc.identifier.urihttp://hdl.handle.net/10222/82186
dc.language.isoenen_US
dc.subjectbayesian networken_US
dc.subjectoil spillsen_US
dc.subjectoil spill responseen_US
dc.subjectresponse effectivenessen_US
dc.subjectCanadian Arcticen_US
dc.subjectIn-Situ Burningen_US
dc.subjectChemical Dispersantsen_US
dc.subjectMechanical Recoveryen_US
dc.subjectsensitivity analysisen_US
dc.subjectcriticality analysisen_US
dc.subjectstrength of evidenceen_US
dc.subjectanalysisen_US
dc.subjectbayesian modelen_US
dc.subjectmodelen_US
dc.titleA Bayesian Network Risk Model for Oil Spill Response Effectiveness in the Canadian Arctic (OSRECA)en_US

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