基于PS-InSAR变形强度的动态山体滑坡易感性测绘
Bijing Jin1, Taorui Zeng2, Kunlong Yin1
1Faculty of Engineering, China University of Geosciences, Wuhan, China.
Environmental science and pollution research international
|January 3, 2024
概括
这项研究通过将动态变形因素与静态条件相结合,增强了山体滑坡易感性测绘. 动态滑坡易感性映射 (DLSM) 框架,特别是使用决策树模型,显著提高了滑坡风险管理的预测准确性.
科学领域:
- 地质科学 地质科学
- 遥感 遥感 遥感 遥感
- 机器学习 机器学习
背景情况:
- 精细的山体滑坡风险管理需要先进的动态易感性建模.
- 三峡水库区的万州运河是重要的交通纽带,面临着严重的山体滑坡风险.
- 现有的模型往往忽略了影响滑坡发生的关键动态因素.
研究的目的:
- 探索动态滑坡易感性建模的扩展相关性框架.
- 开发和验证一个动态的山体滑坡易感测绘 (DLSM) 方法.
- 评估结合动态变形因子对预测准确性的影响.
主要方法:
- 使用了五种机器学习模型:逻辑回归 (LR),多层感知神经网络 (MLPNN),支持矢量机 (SVM),随机森林 (RF) 和决策树 (DT).
- 采用PS-InSAR技术来导出变形强度作为动态因素.
- 集成的动态因素与静态因素 (地形,地质,水文,人类活动) 的DLSM.
主要成果:
- 决策树模型实现了最高的AUC值93.1%,当包含动态因素时增加了2%.
- 整合动态因素提高了几种机器学习模型的预测准确度.
- DLSM的结果显示,与山体滑坡的实际空间分布更好地对齐.
结论:
- 拟议的DLSM框架为评估山体滑坡易感性的静态模型提供了显著的改进.
- 整合动态变形强度可以提高山体滑坡风险管理的可靠性和准确性.
- 这项研究为动态滑坡灾害管理和预防策略提供了宝贵的参考资料.
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