Machine learning-based forecasting of urban fire impact in city environments

Abstract

Objective: This study develops a predictive model to help fire departments improve resource allocation by estimating the likelihood of fire escalation. Methods: We analyzed 47,382 fire incidents from a city in Taiwan, applying an XGBoost model trained on building characteristics, temporal factors, and geographic information system-derived spatial features. The model was validated using 5-fold cross-validation, temporal holdouts, and geographic tests. Results: The model achieved 85.6% accuracy and an AUC of 0.83. Fires were more likely to escalate in older buildings, at night, and on weekends, with building structure, use, and number of floors identified as the strongest predictors. A retrospective simulation suggested that model-informed dispatch could reduce property damage by 25%, firefighter injuries by 21%, and response times by 18%. Implications: These findings demonstrate the potential of predictive analytics to enhance real-time firefighting efficiency and public safety. While promising, the framework requires validation in other cities and with more granular severity scales to ensure broader applicability.