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使用基于度指数的负样本选择策略绘制滑坡易感性映射:卢龙县的案例研究

Kong Yuzhong^1,2, Wu Hua^1,2, Xu Chong^3,4

¹Tibet University, Lhasa, Tibet, China.

|May 9, 2025

概括

这项研究通过IOE模型对非山体滑坡样本进行改进并将其与机器学习集成,从而改善了西藏的山体滑坡易感性测绘. 优化的模型显著提高了预防灾害的预测准确性.

科学领域:

地质科学地质科学
环境科学环境科学
地质地质地质地质地质地

背景情况:

滑坡在喜马拉雅山脉,中国等高海拔地区构成重大地质危险.
这些地区具有挑战性的环境条件阻碍了传统的现场调查以评估山体滑坡.
准确地绘制山体滑坡易感性的地图对于预防灾害和土地利用规划至关重要.

研究的目的:

为西藏卢龙县开发和验证一个改进的滑坡易感性评估模型.
通过使用IOE模型优化非滑坡样本选择来提高滑坡预测的准确性.
为了比较与IOE模型集成的不同机器学习模型的性能,以绘制山体滑坡易感性的地图.

主要方法:

利用谷歌地球卫星图像创建了一个2517起碎片发生的山体滑坡数据库.
确定了12个条件因素,包括地质,地形,气象,水文,植被,土壤和人类活动.
将重叠元素信息 (IOE) 模型与支向量分类 (SVC),多层感知子 (MLP),线性差异分析 (LDA) 和物流回归 (LR) 模型集成.

主要成果:

使用IOE模型优化非滑坡样本显著提高了所有合机器学习模型的性能 (AUC,精度,精度,F1分数).
IOE-MLP模型显示了最高的性能,AUC从0.8172增加到0.9747.
土地使用,海拔和坡度被确定为研究区域中占主导地位的滑坡控制因素.

结论:

对IOE模型与机器学习的整合提供了一种有效的方法来评估山体滑坡的易感性,特别是在数据稀缺的高海拔地区.
IOE-MLP模型提供了卓越的预测准确性和分类性能,用于识别高风险的山体滑坡区域.
这些发现为区域防灾,减灾战略和在类似地质环境中的可持续土地利用规划提供了有价值的数据.

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