Leveraging IoT for Enhanced Incident Response and Safety Monitoring in Firefighting: An Analysis of Firefighter Fatalities and Operational Hazards

The increasing complexity and unpredictability of firefighting environments demand innovative solutions to enhance safety and operational efficiency. This research presents a novel Internet of Things (IoT)-driven framework, integrating predictive analytics and deep learning, to proactively address firefighter fatalities and operational hazards. This study analyzes a dataset of firefighter fatalities, provides insights through exploratory data analysis, and proposes a machine learning-based IoT framework for proactive safety measures. Two predictive models—Random Forests and Deep Neural Networks—are compared for hazard prediction.The IoT framework incorporates real-time data from wearable health sensors, environmental monitors, and equipment telemetry to dynamically assess risk, predict hazards, and optimize incident response. By combining historical patterns with real-time analytics, this approach exemplifies the transformative potential of IoT and machine learning in reducing fatalities, improving situational awareness, and advancing firefighting as a safer, smarter, and data-driven profession.