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使用可解释的框架评估卡尔巴斯山脉的火灾易感性
Melinda Manczinger1, László Kovács2, Tibor Kovács3
1Doctoral School of Economics and Business Informatics, Corvinus University of Budapest, Budapest, 1093, Hungary. melinda.manczinger@uni-corvinus.hu.
Scientific reports
|August 18, 2025
概括
气候变化增加了加尔巴特山脉的火灾风险. 这项研究开发了一种可解释的机器学习框架和数据集,用于绘制火灾易感性的地图,帮助区域社区和决策者.
科学领域:
- 环境科学 环境科学
- 地理空间分析的研究.
- 机器学习 机器学习
背景情况:
- 气候变化正在加剧卡尔巴特地区的火灾风险.
- 有效的消防管理需要准确的易感性评估.
研究的目的:
- 创建一个统一的数据集和可解释的机器学习框架,用于评估卡尔巴特地区的火灾易感性.
- 识别火灾易感性的关键预测因素和空间模式.
主要方法:
- 采用了两阶段的机器学习方法,包括在H2O环境中选择特征和优化模型.
- 为了模型的可解释性,使用了SHAP (SHapley添加式解释) 框架.
- 针对所有七个卡尔巴特国家,编制了一套包含27个变量的统一数据集.
主要成果:
- 该研究确定了卡尔巴特地区八个不同的易受火灾的集群.
- 机器学习模型在评估火灾易感性方面表现出高的预测准确性.
- 通过特征选择和SHAP分析确定了影响火灾风险的关键环境和人为变量.
结论:
- 开发的框架和数据集为卡尔巴斯山火灾易感性提供了有价值的,可解释的见解.
- 调查结果支持地方社区和政策制定者基于证据的决策,以减轻气候变化引起的火灾风险.
- 统一的数据和易感性地图是加强区域火灾管理战略的关键资源.
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