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<p>This study develops an efficient prediction framework for urban fire evolution by integrating GIS-based modeling with a hybrid physical–empirical fire model. The framework significantly reduces computational workload and simulation complexity while maintaining physical interpretability. Validation against real fire incidents confirms its ability to accurately reproduce key fire propagation dynamics, both qualitatively and quantitatively. Simulation results further highlight the critical influence of ignition location and wind conditions on urban fire spread, supporting the formulation of targeted prevention strategies and emergency plans. Notably, the framework boasts high computational efficiency, as it can simulate 48-hour fire scenarios involving thousands of buildings in less than one minute on a standard personal computer. This study provides a practical and scalable tool for urban fire risk assessment, real-time prediction, and resilient urban design.</p>

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An evolving fire prediction framework for urban building clusters with self-adaptive optimization and GIS integration

  • 期刊论文

  • 发表日期：

    2026年6月17日

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  • Bin Sun

摘要

This study develops an efficient prediction framework for urban fire evolution by integrating GIS-based modeling with a hybrid physical–empirical fire model. The framework significantly reduces computational workload and simulation complexity while maintaining physical interpretability. Validation against real fire incidents confirms its ability to accurately reproduce key fire propagation dynamics, both qualitatively and quantitatively. Simulation results further highlight the critical influence of ignition location and wind conditions on urban fire spread, supporting the formulation of targeted prevention strategies and emergency plans. Notably, the framework boasts high computational efficiency, as it can simulate 48-hour fire scenarios involving thousands of buildings in less than one minute on a standard personal computer. This study provides a practical and scalable tool for urban fire risk assessment, real-time prediction, and resilient urban design.