Investigating The Influential Factors On Firefighter Injuries Using Statistical Machine Learning

摘要

Firefighters are the most important resources in protecting the public and responding to emergencies. Canada's first-ever national fire information database (NFID) was implemented in 2017, which enables effective big data analytics to investigate fire and firefighter related issues. This paper proposes principal component analysis and deep neural networks to investigate the influential factors that affect firefighter injuries. The methods have been validated using the data available in NFID. The results are valuable in supporting multicriteria decision making and decision support systems.