Forecasting urban fire severity for enhanced emergency response and resource allocation

This study aimed to develop a predictive model to help fire departments improve resource allocation by estimating the likelihood of fire escalation and integrating GIS data for faster, data-driven decision-making, ultimately enhancing efficiency and public safety. We analyzed 47,382 fire incidents from a city (2010-2020). After cleaning and preprocessing, an XGBoost model was trained and validated using 5-fold cross-validation, and then tested across various temporal and geographic contexts. Key predictive features included building structure, use, number of floors, age, and time of day. The model achieved an accuracy of 82.7%, with a true positive rate of 84.3% for major fires and a true negative rate of 81.1% for ordinary fires. Fires were more likely to escalate in older buildings, during nighttime, and on weekends. Simulation showed potential reductions of 23% in property damage, 18% in firefighter injuries, and 15% in response times through model-guided resource allocation. The study introduces a novel application of predictive analytics in firefighting. Unlike previous studies that primarily focus on statistical fire risk assessment or standalone predictive models, our work uniquely integrates GIS-based spatial data with XGBoost, enabling both high predictive accuracy and spatially informed resource allocation. This dual approach advances real-time decision-making in urban firefighting beyond existing methods.