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预测城市火灾严重程度,以加强应急响应和资源分配
Shao-Lun Lee1, Mei-Hua Hsu2, Yi-Fan Wang3
1Department of Information Management, Asia Eastern University of Science and Technology, New Taipei, Taiwan.
Scientific reports
|November 26, 2025
概括
消防部门可以使用一个新的预测模型来改善资源分配,该模型估计了火灾升级风险. 这种数据驱动的方法,集成地理信息系统 (GIS) 数据,提高公共安全和运营效率.
科学领域:
- 城市规划和公共安全
- 数据科学和预测分析.
- 消防和应急响应工作.
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.
- 现有的方法往往缺乏最佳决策所需的预测准确度和空间整合.
- 火灾升级对财产,消防员安全和公共福祉构成重大风险.
研究的目的:
- 开发和验证火灾升级可能性的预测模型.
- 整合地理信息系统 (GIS) 数据,用于空间信息化资源分配.
- 通过数据驱动的决策来提高城市消防效率和公共安全.
主要方法:
- 从一个主要城市分析了47382起火灾事件 (2010-2020年).
- 使用5倍交叉验证开发和验证XGBoost预测模型.
- 整合关键特征,包括建筑特征,一天中的时间和GIS数据.
主要成果:
- 对于大火和普通火灾,XGBoost模型实现了82.7%的准确性,具有高的真正 (84.3%) 和真负 (81.1%) 率.
- 确定增加火灾升级风险的因素:老建筑,夜间和周末.
- 模拟预计可能减少财产损失 (23%),消防员受伤 (18%) 和响应时间 (15%).
结论:
- 该研究提出了用于城市消防的预测分析 (XGBoost) 和GIS数据的新型集成.
- 与以前的方法相比,这种综合方法提供了更高的预测准确性和空间显式的资源配置.
- 以模型为指导的资源分配有望显著提高消防效率和公共安全结果.
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