Predicting Firefighter Injury and Entrapment in Urban Firefighting Operations: An Investigation Into the Effectiveness of Modified Fire Time Stages and Machine Learning
The generated models can help to reduce the incidence of injuries and entrapments among firefighters by providing the decision-makers with early prediction of the risk factors associated with an ongoing urban firefighting operation.
Authors
- Mohammad Mashreghi (ORCID: https://orcid.org/0000-0002-5197-6260)
- Aynaz Lotfata (ORCID: https://orcid.org/0000-0002-7511-1755)
- Krzysztof J. Cios (ORCID: https://orcid.org/0000-0001-5808-5316)
- Hamed Khosravi (ORCID: https://orcid.org/0000-0003-0378-6291)
- Hamed Tabesh (ORCID: https://orcid.org/0000-0003-3081-0488)
Institutions
- West Virginia University (US)
- Mashhad University of Medical Sciences (IR)
- Virginia Commonwealth University (US)
- University of California, Davis (US)
Publication Details
- Journal
- Health Science Reports
- Published
- 2025-03-01
- DOI
- https://doi.org/10.1002/hsr2.70327
- Primary Topic
- Injury Epidemiology and Prevention
- Type
- article
- Field-Weighted Citation Impact
- 0.00