基于机器学习的预测城市环境中的城市火灾影响
Shao-Lun Lee1, Mei-Hua Hsu2, Yi-Fan Wang3
1Department of Information Management, Asia Eastern University of Science and Technology, New Taipei.
Science progress
|December 9, 2025
概括
这项研究开发了一个预测模型,通过估计火灾升级风险来改善消防部门的资源配置. 该模型可以显著减少财产损失,消防员受伤和响应时间.
科学领域:
- 灾害管理 灾害管理
- 人工智能的人工智能
- 城市安全城市安全
背景情况:
- 对消防部门来说,有效的资源配置对于消防部门有效管理事件至关重要.
- 预测火灾升级可以显著改善响应结果并减少相关损害.
研究的目的:
- 开发和验证用于估计火灾升级可能性的预测模型.
- 通过数据驱动的洞察力,为消防部门的资源分配策略提供信息.
主要方法:
- 使用XGBoost模型分析了47382起火灾事件.
- 整合建筑特征,时间数据和GIS空间特征.
- 通过5倍交叉验证,时间保留和地理测试进行验证.
主要成果:
- 预测模型实现了85.6%的准确性和0.83.3的AUC.
- 火灾升级的关键预测因素包括旧建筑,夜间/周末事件,建筑结构,使用和楼层数量.
- 模拟显示,物业损失可能减少25%,消防员受伤可能减少21%,响应时间可能减少18%.
结论:
- 预测分析为提高实时消防效率和公共安全提供了强大的工具.
- 开发的框架显示了优化资源配置和事件管理的前景.
- 为了更广泛的适用性,建议在不同的城市环境和精细的严重程度尺度中进行进一步的验证.
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