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综合性野火危险模型和因素:一项审查
Ioannis Zacharakis1, Vassilios A Tsihrintzis1
1Centre for the Assessment of Natural Hazards and Proactive Planning & Laboratory of Reclamation Works and Water Resources Management, School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, 9 Heroon Polytechniou St., Zographou 15780, Athens, Greece.
The Science of the total environment
|July 24, 2023
概括
本综述分析了野火危险建模,发现机器学习在预测火灾发生方面优于经典统计数据. 关键的风险因素包括气象,植被,地形,水文,社会经济,土地利用和气候.
科学领域:
- 环境科学 环境科学
- 计算科学 计算科学
- 风险管理 风险管理
背景情况:
- 野火研究始于20世纪50年代,计算方法在21世纪进步.
- 当局不充分利用现代野火危险评估方法,特别是人为火灾的概率模型.
- 缺乏对野火危险研究和综合建模方法的全面审查.
研究的目的:
- 审查和分析森林火灾危险估计的综合建模方法.
- 确定影响野火发生的关键驱动因素.
- 为综合性野火危险风险系统 (IWDRS) 提出最佳方法和关键风险因素.
主要方法:
- 在主要的科学数据库 (Scopus,谷歌学者等) 中进行文献搜索. ) 的情况.
- 230项选定的研究按技术进行分类:GIS,遥感,统计,机器学习,模拟和其他.
- 对研究子集的元分析,以评估方法性能和变量重要性.
主要成果:
- 机器学习技术在预测野火危险方面通常优于传统的统计方法.
- 机器学习模型的可预测性取决于输入数据的质量和数量.
- 地理信息系统 (GIS) 和遥感是有价值的补充工具,而模拟建模在火灾行为预测方面表现出色.
结论:
- 野火危险受到七个关键变量群的影响:气象,植被,地形,水文,社会经济,土地利用和气候.
- 已经确定了95个对野火危险的具体解释性驱动因素.
- 对可能有用但较少研究的建模技术还需要进一步的研究.
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