Designing a Bayesian urgency assessment tool for search and rescue in the Canadian Arctic
Peters, Joshua and Quigley, John and Rudman, Archie and Belton, Ian and Howick, Susan and Kikkert, Peter and Walls, Lesley; (2025) Designing a Bayesian urgency assessment tool for search and rescue in the Canadian Arctic. In: Proceedings of the 22nd ISCRAM Conference. ISCRAM, CAN. (https://doi.org/10.59297/zgw11c50)
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Abstract
Effective urgency assessment is critical for search and rescue (SAR) operations, particularly in remote regions such as the Canadian Arctic. Vast distances, severe weather, and limited resources present significant challenges in Nunavut and Nunavik. Existing urgency assessment frameworks, while effective in other contexts, are often unsuitable for Arctic ground SAR. This paper reviews existing urgency assessment frameworks, including SAR-specific systems and Bayesian network (BN) approaches, assessing their applicability to the Arctic context. It further explores the potential for developing a BN-based urgency assessment tool tailored to ground SAR in Nunavut and Nunavik. We discuss key factors that such a model might incorporate—such as environmental conditions, shelter availability, and local knowledge—and highlight the benefits of probabilistic reasoning in supporting decision-making and optimising resource allocation. While a fully realised prototype is not yet presented, this research outlines the conceptual groundwork for future development. The ultimate aim is to improve decision support for SAR coordinators, risk communication, and the overall effectiveness of Arctic SAR efforts.
ORCID iDs
Peters, Joshua
ORCID: https://orcid.org/0009-0008-3047-6399, Quigley, John
ORCID: https://orcid.org/0000-0002-7253-8470, Rudman, Archie
ORCID: https://orcid.org/0009-0003-7119-4498, Belton, Ian
ORCID: https://orcid.org/0000-0002-2479-6563, Howick, Susan
ORCID: https://orcid.org/0000-0002-0796-7981, Kikkert, Peter and Walls, Lesley
ORCID: https://orcid.org/0000-0001-7016-9141;
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Item type: Book Section ID code: 93053 Dates: DateEvent17 May 2025PublishedSubjects: Social Sciences > Industries. Land use. Labor > Risk Management
Science > Mathematics > Probabilities. Mathematical statisticsDepartment: Strathclyde Business School > Management Science
Faculty of Engineering > Civil and Environmental EngineeringDepositing user: Pure Administrator Date deposited: 09 Jun 2025 09:53 Last modified: 05 Apr 2026 00:27 URI: https://strathprints.strath.ac.uk/id/eprint/93053
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