The use of machine learning for accidental dwelling fire prevention

Taylor, M ORCID: 0000-0002-5647-426X, Dean, E, Fielding, J, Lyon, R ORCID: 0000-0003-3776-2087, Reilly, D, Francis, H and Kwasnica, V The use of machine learning for accidental dwelling fire prevention. Fire safety journal. ISSN 0379-7112 (Accepted)

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Abstract

In this article the use of machine learning for fire prevention support is examined over the period 2010 to 2024 based on a case study in a fire and rescue service in Northwest England. Machine learning was used to develop a multiple linear regression model of accidental dwelling fire risk at the Lower Super Output Area of geography. This was enhanced by using machine learning to develop a k-means cluster analysis model of communities at the finer grained Output Area level. Over the study period the percentage decrease in accidental dwelling fires in the area studied was 44.2% compared to a decrease of 27.5% in England as a whole which appeared to indicate that the more precise targeting of fire prevention resulting from statistical models using machine learning had a positive effect on the effectiveness of fire prevention activities.

Item Type:	Article
Uncontrolled Keywords:	0904 Chemical Engineering; 0911 Maritime Engineering; Civil Engineering; 4005 Civil engineering
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Computer Science and Mathematics
Publisher:	Elsevier
Date of acceptance:	19 August 2025
Date Deposited:	20 Aug 2025 09:14
Last Modified:	20 Aug 2025 09:15
URI:	https://researchonline.ljmu.ac.uk/id/eprint/26955

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