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Predicting Firefighter Injury and Entrapment in Urban Firefighting Operations: An Investigation Into the

Mohammad Mahdi Barati Jozan^1,2, Hamed Khosravi³, Aynaz Lotfata⁴

¹Department of Medical Informatics, School of Medicine Mashhad University of Medical Sciences Mashhad Iran.

Health Science Reports

|March 5, 2025

Summary

Machine learning models accurately predict firefighter injuries and entrapment during urban fires. This technology enhances safety by providing early risk assessments for emergency responders.

Keywords:

firefighters injuries and entrapment machine learning predictive modeling safety

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Area of Science:

Fire Safety Engineering
Computational Risk Assessment
Emergency Response Management

Background:

Urban fires present significant risks to property and life.
Firefighter safety is paramount in emergency response operations.
Predicting firefighter injury and entrapment is crucial for effective incident management.

Purpose of the Study:

To develop predictive models for firefighter injury and entrapment during urban fires.
To assess the efficacy of machine learning algorithms in this prediction task.
To enable early risk identification before or during initial firefighting operations.

Main Methods:

Comparison of eight machine learning algorithms.
Utilized data from the Fire Department Operations and Management System (FOMS).
Models were evaluated through five stages, incorporating built-in FOMS functions for feature calculation.

Main Results:

Multi-Layer Perceptron achieved 96.7% predictive accuracy for firefighter injuries.
Kernel Naive Bayes achieved 96.0% predictive accuracy for firefighter entrapment.
Both algorithms demonstrated high efficacy in predicting critical firefighter safety events.

Conclusions:

Developed machine learning models can significantly reduce firefighter injuries and entrapments.
Early prediction of risk factors aids decision-makers in urban firefighting.
Enhanced situational awareness improves safety outcomes for emergency personnel.