使用随机森林利用卫星观测和地形特征绘制洪水易感性的地图
Jorge Saavedra Navarro1, Ruodan Zhuang1, Cinzia Albertini1
1Department of Civil, Construction and Environmental Engineering (DICEA), University of Naples Federico II, Naples, Italy.
The Science of the total environment
|September 25, 2025
概括
这项研究通过使用随机森林模型和26个洪水条件因素,增强了意大利的洪水易感性测绘. 最好的模型,包括地形洪水指数,提高了准确性,有助于风险管理.
科学领域:
- 环境科学 环境科学
- 地质科学 地质科学
- 数据科学数据科学数据科学
背景情况:
- 洪水事件带来了重大破坏性风险,需要有效的风险管理策略.
- 准确地绘制洪水易受性的地图对于社会和环境保护至关重要.
研究的目的:
- 用随机森林 (RF) 模型评估意大利的洪水易感性.
- 评估26个洪水条件因素 (FCF) 的有效性,并确定最佳预测器集.
- 提高洪水易发地区识别的准确性和效率.
主要方法:
- 采用随机森林 (RF) 模型进行洪水易感性评估.
- 利用信息的平均价值 (AMI) 来最大限度地利用FCF信息和皮尔森相关性/VIF来解决对线性.
- 通过卫星观测和历史洪水记录对模型进行校准;根据官方地图对11个因子集 (SoFs) 进行评估.
主要成果:
- 使用SoF-1训练的射频模型 (包括平均最大每日降水量,地形洪水指数 (GFI),与河流的距离,海拔,岩石学,土壤,NDVI,土地覆盖) 显示出优异的概括性.
- 包括GFI在内显著提高了预测准确性,特别是在以前未被绘制的区域.
- 在平坦地形和数据稀缺地区预测易感性仍然存在挑战.
结论:
- 整合卫星数据,互补数据集和优化预测器 (如GFI) 可以提高洪水易感性绘图的准确性.
- 开发的方法简化了初步分析的计算过程.
- 调查结果为洪水风险管理决策者提供了宝贵的见解.
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