印度全国范围的山体滑坡易感性和风险映射使用混合数据驱动方法
Imran Khan1, Harish Bahuguna2, Ashutosh Kainthola3
1Geoengineering and Computing Laboratory, Department of Geology, Banaras Hindu University, Varanasi, 221005, India.
Scientific reports
|December 31, 2025
概括
这项研究使用三个模型为印度提供了高分辨率的山体滑坡易感性和风险地图. 圣诞节 (Yule) 是一个节日.
科学领域:
- 地质科学 地质科学
- 天然危害 天然危害
- 地质形态学 地质形态学
背景情况:
- 滑坡在印度多样化的地形构成一个重大和反复出现的自然危险.
- 有效地减轻山体滑坡风险和政策决策需要全面的,高分辨率的国家级评估.
- 在印度国家一级的详细的山体滑坡易感性和风险绘制中存在一个关键的研究缺口.
研究的目的:
- 为印度进行高分辨率 (90米×90米) 全国范围的山体滑坡易感性和风险评估.
- 为了比较三个不同的建模方法的性能:分析层次过程 (AHP),频率比 (FR) 和尤尔系数 (Yc).
- 通过现场验证和地图解释,确定最可靠的土地滑坡易感性和风险映射方法.
主要方法:
- 利用了对109,504个记录的事件进行全面的山体滑坡清单.
- 通过专家判断选择的内置的因果因素.
- 采用并验证了三种统计模型 (AHP,FR,Yc) 用于灵敏度映射,并使用ROC和精度回忆曲线评估预测性能.
主要成果:
- 确定了非常高的山体滑坡易感区,覆盖印度4.0% (AHP),4.2% (Yc) 和4.5% (FR).
- 在所有模型中,高和非常高的敏感性区域的组合范围从10.4%到11.0%.
- 尤尔系数 (Yc) 模型被认为是最具代表性的,将0.34万平方公里分类为高至非常高的易感性,并确定8,606平方公里具有高至极高的山体滑坡风险.
结论:
- 该研究成功地为印度生成了高分辨率的国家山体滑坡易感性和风险地图.
- 模型比较显示了AHP,FR和Yc的强大预测性能,其中Yc提供了最具地面代表性的输出.
- 这些发现为加强印度易受灾害地区的灾害风险降低策略,山体滑坡预测,缓解和弹性规划提供了关键数据.
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