在紧急情况下使用时间序列预测方法
P Villoria Hernandez1, I Mariñas-Collado2, A Garcia Sipols3
1Department of Electronics, Rey Juan Carlos University, Madrid, Spain.
Scientific reports
|September 26, 2023
概括
使用传感器数据,HelpResponder可以在低可见性条件下检测火灾热点. 该系统通过改善消防响应时间和挽救生命来帮助紧急干预队 (EI).
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 环境科学 环境科学
背景情况:
- 消防紧急情况在识别热点,定位紧急团队,追踪火灾蔓延和规划疏散路线方面存在关键挑战.
- 由于高温造成的可见性不足,使紧急响应人员的这些关键任务变得复杂.
研究的目的:
- 开发和评估HelpResponder,HelpResponder是一个用于检测火灾环境中可见度有限的感兴趣区域的系统.
- 确定火灾场景中环境变量最有效的预测模型.
- 创建一个节能预测系统来节省电池.
主要方法:
- 利用来自消防塔的传感器数据,包括温度,湿度和空气质量.
- 应用统计和机器学习模型:ARIMAX,KNN,SVM和TBATS用于变量建模.
- 提出了一个增强的SVM模型,包括时间结构,并探索了模型组合,以改进预测.
主要成果:
- 评估多个预测模型,以确定最适合测量环境数据的模型.
- 证明了将不同的预测模型结合起来,可以获得最高的效率.
- 在模拟的真实世界紧急情况中验证了HelpResponder系统在敌对的建筑环境中.
结论:
- 在HelpResponder系统有效地检测火灾热点在具有挑战性的,低可见性条件下.
- 开发的系统通过提供关键的实时信息来提高消防员的响应速度.
- 这项技术减少了与信息缺口相关的风险,并优化了战术行动,可能挽救生命.
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