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Updated: May 17, 2025

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基于双变量统计和多标准决策分析的小规模森林火灾风险区分
Yi-Yun Ouyang1,2, Chun-Hui Li1,2, Rong-Yu Ni1,2
1College of Fores-try, Fujian Agricultural and Forestry University, Fuzhou 350002, China.
概括
贵州省旺莫县的森林火灾风险绘制地图,使用联合统计和决策分析模型确定了高风险地区. 这些新模型 (WOE-ANP,SI-ANP) 为有效的森林火灾管理提供了准确可靠的决策支持.
科学领域:
- 林业林业 林业 林业 林业
- 环境科学 环境科学
- 地理信息系统 (GIS) 是指地理信息系统.
背景情况:
- 森林火灾对人类生命,生态系统和生物多样性构成重大风险.
- 有效的森林火灾管理需要准确的,小规模的区域风险映射.
- 以前的方法可能缺乏全面的分析能力来准确评估风险.
研究的目的:
- 开发和评估森林火灾风险区分的全新综合模型.
- 为了提高准确性,将双变量统计方法与多标准决策分析相结合.
- 为贵州省王莫县森林火灾管理提供可靠的决策支持.
主要方法:
- 综合双变量统计 (证据权重 - WOE,统计指数 - SI) 与多标准决策分析 (分析层次过程 - AHP,分析网络过程 - ANP).
- 开发并应用新的WOE-ANP和SI-ANP森林火灾风险区分的综合模型.
- 使用现有数据验证了模型准确性,并将ANP与AHP的性能进行了比较.
主要成果:
- 在南方,西部和北部的旺莫县确定了重要的森林火灾易发地区,其中39.2%的地区被归类为4级风险或更高的风险级别.
- 综合性WOE-ANP和SI-ANP模型显示出高预测准确度 (分别为84.3%和83.8%).
- 与分析层次流程 (AHP) 相比,分析网络流程 (ANP) 提供了更可靠的森林火灾风险因素权重.
结论:
- 开发的WOE-ANP和SI-ANP模型显著提高了森林火灾风险评估的预测能力.
- 在森林火灾分析中,ANP提供了一种更强大的方法来确定风险因素的相对重要性.
- 这些准确可靠的模型为有效的森林火灾管理策略提供了至关重要的决策支持.
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