在ENSO El nino阶段使用机器学习方法的班加利斯摄政区泥炭地火灾的脆弱性
Fauziah1, Lilik B Prasetyo2, Nonon Saribanon3
1Department of Magister Technology Information, Faculty of Information and Communications Technology, Nasional University, Jl. Sawo Manila No. 61 RT.14/ RW.7, West Pejaten, Pasar Minggu, South Jakarta 12520, Indonesia.
MethodsX
|January 20, 2025
概括
印度尼西亚的泥炭火灾越来越令人担忧. 这项研究开发了一种高度准确的随机森林模型,用于预测班加利斯县的火灾脆弱性,并确定了关键影响因素.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 地理空间分析的研究.
背景情况:
- 泥炭火灾是印度尼西亚经常出现的环境问题,特别是在苏门答腊的班加利斯县.
- 越来越多的频率需要有效的泥炭火灾预测模型.
研究的目的:
- 通过2019年燃烧面积数据,确定本加利斯县泥炭地火灾的脆弱性.
- 为了比较随机森林 (RF) 和物流回归 (Log-Reg) 算法的火灾预测效果.
主要方法:
- 利用MODIS卫星数据进行2019年燃烧区域映射.
- 采用随机森林 (RF) 和物流回归 (Log-Reg) 算法.
- 包括独立变量,如生理学,泥炭特性,人为因素,气候和规范差异湿度指数 (NDMI).
主要成果:
- 2019年,班加利斯县的燃烧总面积为175.85平方公里,鲁帕特区受影响最严重.
- 随机森林模型表现出卓越的性能,AUC为0.972和95.07%的准确性.
- 影响泥炭火灾的关键因素包括道路密度,降水,排水密度,NDMI和河流密度.
结论:
- 随机森林模型有效地预测了班加利斯县的泥炭地火灾脆弱性.
- 确定了三个脆弱级别:非脆弱,低脆弱和高脆弱.
- 这些发现支持在高风险地区制定有针对性的消防管理策略.
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