| dc.contributor.author | Al Sharkawi, Talah | |
| dc.date.accessioned | 2022-12-19T18:23:27Z | |
| dc.date.available | 2022-12-19T18:23:27Z | |
| dc.date.issued | 2022-12-16 | |
| dc.identifier.uri | http://hdl.handle.net/10222/82186 | |
| dc.description | Throughout 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.abstract | 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 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 Burning | en_US |
| dc.language.iso | en | en_US |
| dc.subject | bayesian network | en_US |
| dc.subject | oil spills | en_US |
| dc.subject | oil spill response | en_US |
| dc.subject | response effectiveness | en_US |
| dc.subject | Canadian Arctic | en_US |
| dc.subject | In-Situ Burning | en_US |
| dc.subject | Chemical Dispersants | en_US |
| dc.subject | Mechanical Recovery | en_US |
| dc.subject | sensitivity analysis | en_US |
| dc.subject | criticality analysis | en_US |
| dc.subject | strength of evidence | en_US |
| dc.subject | analysis | en_US |
| dc.subject | bayesian model | en_US |
| dc.subject | model | en_US |
| dc.title | A Bayesian Network Risk Model for Oil Spill Response Effectiveness in the Canadian Arctic (OSRECA) | en_US |
| dc.date.defence | 2022-12-09 | |
| dc.contributor.department | Department of Industrial Engineering | en_US |
| dc.contributor.degree | Master of Applied Science | en_US |
| dc.contributor.external-examiner | n/a | en_US |
| dc.contributor.graduate-coordinator | Dr. John Blake | en_US |
| dc.contributor.thesis-reader | Dr. Ron Pelot | en_US |
| dc.contributor.thesis-reader | Dr. Hamid Afshari | en_US |
| dc.contributor.thesis-reader | Dr. Haibo Niu | en_US |
| dc.contributor.thesis-supervisor | Dr. Floris Goerlandt | en_US |
| dc.contributor.ethics-approval | Received | en_US |
| dc.contributor.manuscripts | Not Applicable | en_US |
| dc.contributor.copyright-release | Not Applicable | en_US |
