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Understand Daily Fire Suppression Resource Ordering and Assignment Patterns by Unsupervised Learning

1
Department of Forest and Rangeland Stewardship, Colorado State University, Fort Collins, CO 80523, USA
2
Rocky Mountain Research Station, USDA Forest Service, Fort Collins, CO 80526, USA
3
Rocky Mountain Research Station, USDA Forest Service, Missoula, MT 59801, USA
*
Author to whom correspondence should be addressed.
Mach. Learn. Knowl. Extr. 2021, 3(1), 14-33; https://0-doi-org.brum.beds.ac.uk/10.3390/make3010002
Received: 14 November 2020 / Revised: 10 December 2020 / Accepted: 18 December 2020 / Published: 23 December 2020
(This article belongs to the Section Learning)
Wildland fire management agencies are responsible for assigning suppression resources to control fire spread and mitigate fire risks. This study implements a principle component analysis and an association rule analysis to study wildland fire response resource requests from 2016 to 2018 in the western US to identify daily resource ordering and assignment patterns for large fire incidents. Unsupervised learning can identify patterns in the assignment of individual resources or pairs of resources. Three national Geographic Area Coordination Centers (GACCs) are studied, including California (CA), Rocky Mountain (RMC), and Southwest (SWC) at both high and low suppression preparedness levels (PLs). Substantial differences are found in resource ordering and assignment between GACCs. For example, in comparison with RMC and SWC, CA generally orders and dispatches more resources to a fire per day; CA also likely orders and assigns multiple resource types in combination. Resources are more likely assigned to a fire at higher PLs in all GACCs. This study also suggests several future research directions including studying the causal relations behind different resource ordering and assignment patterns in different regions. View Full-Text
Keywords: wildland fire; suppression; principle component analysis; association rule analysis wildland fire; suppression; principle component analysis; association rule analysis
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MDPI and ACS Style

Wei, Y.; Thompson, M.P.; Belval, E.J.; Calkin, D.E.; Bayham, J. Understand Daily Fire Suppression Resource Ordering and Assignment Patterns by Unsupervised Learning. Mach. Learn. Knowl. Extr. 2021, 3, 14-33. https://0-doi-org.brum.beds.ac.uk/10.3390/make3010002

AMA Style

Wei Y, Thompson MP, Belval EJ, Calkin DE, Bayham J. Understand Daily Fire Suppression Resource Ordering and Assignment Patterns by Unsupervised Learning. Machine Learning and Knowledge Extraction. 2021; 3(1):14-33. https://0-doi-org.brum.beds.ac.uk/10.3390/make3010002

Chicago/Turabian Style

Wei, Yu, Matthew P. Thompson, Erin J. Belval, David E. Calkin, and Jude Bayham. 2021. "Understand Daily Fire Suppression Resource Ordering and Assignment Patterns by Unsupervised Learning" Machine Learning and Knowledge Extraction 3, no. 1: 14-33. https://0-doi-org.brum.beds.ac.uk/10.3390/make3010002

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