Designing a Bayesian Urgency Assessment Tool for Search and Rescue in the Canadian Arctic
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.
Authors
- Joshua Peters
- Archie Rudman (ORCID: https://orcid.org/0009-0003-7119-4498)
- John Quigley (ORCID: https://orcid.org/0000-0002-7253-8470)
- Peter Kikkert (ORCID: https://orcid.org/0000-0003-1338-3648)
- Lesley Walls (ORCID: https://orcid.org/0000-0001-7016-9141)
- Susan Howick (ORCID: https://orcid.org/0000-0002-0796-7981)
- Ian Belton (ORCID: https://orcid.org/0000-0002-2479-6563)
Institutions
- University of Strathclyde (GB)
- St. Francis Xavier University (CA)
Publication Details
- Journal
- Proceedings of the ... International ISCRAM Conference
- Published
- 2025-05-17
- DOI
- https://doi.org/10.59297/zgw11c50
- Citations
- 1
- Primary Topic
- Oil Spill Detection and Mitigation
- Type
- article
- Field-Weighted Citation Impact
- 8.82