在体育场疏散人群使用火灾报警预测
Afnan A Alazbah1, Osama Rabie1,2, Abdullah Al-Barakati1
1Information Systems Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Sensors (Basel, Switzerland)
|May 14, 2025
概括
这项研究介绍了一种人工智能驱动的预测性消防报警系统,该系统使用实时传感器数据预测火灾危险在点火之前. EvacuNet模型显著提高了火灾检测速度和高密度场所的疏散效率.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 公共安全公众安全.
背景情况:
- 传统的火灾报警器是反应性的,导致响应延迟,并在紧急情况下增加恐慌.
- 像体育场这样的高密度环境需要先进的解决方案来实现高效和安全的疏散.
研究的目的:
- 开发和评估一个人工智能驱动的预测性消防报警和疏散模型,用于高占用场所.
- 通过在点火之前预测火灾危险来提高应急响应效率和公共安全.
主要方法:
- 利用机器学习算法与实时环境传感器数据 (62,630次测量,15个参数).
- 对比了六种模型,包括物流回归,SVM,随机森林和拟议的EvacuNet.
- 专注于早期的火灾风险指标,如温度,湿度,TVOC,CO2和颗粒物.
主要成果:
- EvacuNet实现了卓越的性能,准确率为99.99%,精度为1.00,回忆率为1.00.
- 预测系统显著减少了错误报警,并提高了火灾检测速度.
- 人工智能驱动的疏散优化将拥堵降至最低,减少疏散时间.
结论:
- 基于人工智能的预测建模大大提高了大规模场馆的消防响应和疏散效率.
- 智能火灾检测系统对于提高高占用环境中的公共安全至关重要.
- 未来的研究应该整合物联网,强化学习和实时人群管理,以提高预测准确度.
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