根据MCMC模型,在宁市增加了山体滑坡数据样本和进行了山体滑坡易感性分析
Lianchen Liu1, Peiqing Wang2,3, Libin Su4,5
1College of Water Conservancy and Civil Engineering, Xizang Agriculture and Animal Husbandry University, Linzhi, 860000, China.
Scientific reports
|July 15, 2025
概括
使用合成数据生成改进了有限的山体滑坡数据. 这有助于加强山区的山体滑坡易感性分析和风险评估.
科学领域:
- 地质科学 地质科学
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
背景情况:
- 滑坡易感性分析对于在山区减轻灾难至关重要.
- 有限的山体滑坡样本数据对准确的建模提出了重大挑战.
- 现有的方法在复杂的地形中经常因数据稀缺而扎.
研究的目的:
- 改进尼奇市的山体滑坡易感性分析,尽管样本数据有限.
- 开发和验证一种合成增强山体滑坡数据集的方法.
- 评估增强数据对山体滑坡易感性建模的影响.
主要方法:
- 从最初的11个因素中选择了8个关键影响因素 (例如,高度,角度,道路距离).
- 采用马尔科夫链蒙特卡洛 (MCMC) 方法来生成合成滑坡样本.
- 使用支持矢量机 (SVM) 验证样品质量,并使用光梯度增强机 (LightGBM) 进行建模.
主要成果:
- 支持矢量机 (SVM) 通过MCMC增强数据实现了97.3%的准确性.
- 以曲线下的面积 (AUC) 测量的光梯度增强机 (LightGBM) 模型性能,通过增强数据进行了改进.
- 道路距离,侧面和高度被确定为对山体滑坡易感性的最有影响的因素.
结论:
- 马尔科夫链蒙特卡洛 (MCMC) 方法有效地增加了有限的山体滑坡数据.
- 使用增强数据可以提高山体滑坡易感模型的准确性和可靠性.
- 这项研究为稀缺数据的山区的山体滑坡风险评估提供了宝贵的方法见解.
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