Interpretable Machine Learning for Urban Fire Hazard Assessment with POI Integration

With the rapid advancement of urbanization, the frequency of urban fire incidents has been steadily increasing, posing significant challenges to public safety and sustainable urban development. Understanding the complex relationships between various urban built environments is essential for improving fire risk assessment and management. In this study, we propose an interpretable machine learning framework that incorporates Point of Interests (POIs) to enhance the accuracy and transparency of urban fire risk prediction. Using POI data from Hangzhou, a major megacity in China, we constructed and optimized three machine learning models. Among them, the Light Gradient Boosting Machine (LightGBM) demonstrated the highest prediction accuracy. To address the interpretability of the model, we employed SHapley Additive exPlanations (SHAP) to analyze the contributions of different POI types to fire incident prediction. Our findings reveal that the density of Residential areas and Life Service facilities exerts the most significant influence on fire incidence, suggesting a strong correlation between fire risk, population density, and economic activity. Moreover, interaction effects between different POI types further contribute to the complexity of urban fire risk, indicating the need for integrated and context-specific prevention strategies. This study provides a transparent, data-driven approach to assessing urban fire risk, offering valuable insights for urban planners, policymakers, and emergency service managers. By elucidating the spatial distribution patterns of fire risk in relation to POI density, our model supports more informed decision-making aimed at enhancing urban resilience and sustainability.