机器学习估计了检测时间对灭火成本的影响
Michael Shucheng Huang1, Bruno Wichmann1
1Department of Resource Economics and Environmental Sociology, University of Alberta, Edmonton, Alberta, Canada.
PloS one
|November 20, 2024
概括
减少野火报告延迟提供了最小的成本节省. 检测时间缩短一小时只能减少0.25%的抑制成本,这表明仅检测系统的投资回报有限.
科学领域:
- 环境科学 环境科学
- 林业科学 林业科学
- 风险管理 风险管理
背景情况:
- 气候变暖加剧了野火风险,需要有效的灭火策略和及时检测.
- 准确的野火检测对于有效的野火管理,行为监测和公共安全疏散至关重要.
- 越来越需要量化与野火管理投资相关的经济效益和成本节约.
研究的目的:
- 通过使用火灾级别数据,分析野火报告延迟和灭火成本之间的关系.
- 孤立报告延迟对森林火灾灭火总支出的财务影响.
- 评估旨在改进野火检测系统的投资的成本效益.
主要方法:
- 利用了加拿大西部2015-2020年期间的火灾水平数据.
- 采用机器学习和正交法化技术来区分报告延迟的影响和环境因素.
- 量化了野火报告时间与相关的灭火成本之间的相关性.
主要成果:
- 发现,报告野火的延迟仅占灭火总成本的3%.
- 报告延迟减少了一个小时,导致压制成本减少了0.25%.
- 仅仅通过降低抑制成本的经济效益并不能证明对更快的检测系统的投资是合理的.
结论:
- 仅仅旨在减少报告延迟的野火检测系统的投资,在经济上是不合理的,基于抑制成本的节省.
- 虽然迅速发现对于野火管理至关重要,但通过减少灭火成本,其直接的财务回报是有限的.
- 进一步的研究可能会探索改进检测的其他好处,例如减少环境破坏或提高安全性,以证明投资的合理性.
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