以信任为导向的方法,通过机器学习来增强早期森林火灾检测
Tayyab Khan1, Karan Singh2, Bhoopesh Singh Bhati1
1Indian Institute of Information Technology Sonepat, Khewra, Haryana, India.
Scientific reports
|April 25, 2025
概括
本研究介绍了使用无线传感器网络和机器学习进行森林火灾早期检测的通用信任模型 (UTM). 该系统提高了可靠性,并缩短了有效预防森林火灾的检测时间.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 森林火灾对生态系统和人类社区构成重大威胁.
- 早期检测对于减轻不利的环境和气候影响至关重要.
- 现有的检测系统需要提高可靠性和速度.
研究的目的:
- 为早期森林火灾检测 (FFD) 开发一个实时的通用信任模型 (UTM).
- 提高森林火灾识别系统的可靠性和减少检测时间.
- 将智能无线传感器网络 (WSN) 与机器学习集成,以实现强大的火灾检测.
主要方法:
- 实现了一个智能WSN与集群传感器节点进行广泛的森林覆盖.
- 开发了一个基于传感器节点的通信,能源和数据因素计算信任评级的UTM.
- 利用机器学习回归模型分析温度,湿度和二氧化碳,以提高检测精度.
主要成果:
- 拟议的UTM系统显示了高的数据处理速度.
- 与现有系统相比,在火灾检测方面实现了较短的时间延迟.
- 用7200个样本进行实验验证,证实了该系统在早期森林火灾检测方面的有效性.
结论:
- 该UTM系统为早期森林火灾检测提供了强大而准确的解决方案.
- 将信任机制与机器学习相结合,大大提高了火灾检测能力.
- 该系统是迅速发现和预防森林火灾的有希望的解决方案,特别是在具有挑战性的条件下.
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