Data Analytics and Machine Learning: Revolutionizing Fire Safety and Compliance for U.S. Fire Departments

Adeyanju Adetoro
Independent Researcher
DOI – http://doi.org/10.37502/IJSMR.2021.4411

Abstract

This study explores the transformative impact of data analytics and machine learning on fire safety and compliance for U.S. fire departments. Leveraging advanced techniques such as predictive modeling, risk assessment, and geographic information systems (GIS), the research highlights how these tools can enhance decision-making, optimize resource allocation, and improve fire prevention and response strategies. The study identifies key correlations between fire incidents and factors such as weather conditions, building characteristics, and prevention measures, emphasizing the role of data-driven approaches in mitigating fire risks. Recommendations include the development of predictive models, enhanced GIS applications, and integration of weather data into risk assessments. Future research directions suggest expanding predictive models, incorporating emerging technologies like IoT and drones, and conducting longitudinal studies to evaluate the long-term effects of data-driven interventions. Integrating data analytics and machine learning holds significant promise for advancing fire safety and compliance, ultimately leading to more effective fire management practices and improved public safety.

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