Predicting Firefighter Injury and Entrapment in Urban Firefighting Operations: An Investigation Into the Effectiveness of Modified Fire Time Stages and Machine Learning.

ABSTRACT:

Background and aims

Urban fires pose a significant threat in terms of property damage and potential loss of life. Firefighters play an important role in managing these incidents, so their safety is a top priority for fire departments and emergency responders. The purpose of this study is to predict the probability of firefighter injury and entrapment before dispatching the operational team or the initial stages of the urban firefighting operations.

Methods

In this study, we compare the performance of eight machine learning algorithms in predicting the occurrence of firefighter injuries and entrapment during urban fire incidents. We use data from the Fire Department Operations and Management System (FOMS) of Mashhad's city fire and safety services organization. Specifically,

SUBMITTER: Barati Jozan MM 

PROVIDER: S-EPMC11876003 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature