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地理加权随机森林考虑土地滑坡易感性评估的空间异质性:江县的一个案例研究
Weiheng Qian1,2, Mengyao Shi1,2, Cheng Huang3
1Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
本研究引入了地理加权随机森林 (GWRF) 模型,以改进山区的山体滑坡易感性测绘. 该GWRF模型更好地捕捉空间变化,优于用于准确评估山体滑坡风险的传统方法.
科学领域:
- 地质科学 地质科学
- 地理信息科学 地理信息科学
- 环境科学 环境科学
背景情况:
- 土地滑坡易感性映射 (LSM) 对于减轻危险至关重要.
- 全球模型往往无法捕捉复杂地形中的空间异质性.
- 现有的方法难以应对因子和滑坡之间的空间关系的变化.
研究的目的:
- 通过纳入空间异质性来增强滑坡易感性评估.
- 为LSM开发和验证一个地理加权随机森林 (GWRF) 模型.
- 将GWRF的性能与传统的随机森林 (RF) 和XGBoost模型进行比较.
主要方法:
- 利用地理加权随机森林 (GWRF) 模型来解释空间异质性.
- 在江县使用多种条件因素 (地形,人为).
- 使用InSAR衍生变形,现场调查和无人机观测的验证结果.
主要成果:
- 该GWRF模型表现出卓越的性能,AUC为0.922.2.
- GWRF揭示了比全球模型更详细的土地滑坡易感性的空间模式.
- 由GWRF识别的高度敏感区域与观察到的变形有更好的相关性.
结论:
- 空间异质性对于准确地评估滑坡易感性至关重要.
- 该GWRF模型为LSM在复杂的山区提供了适应性和有效的方法.
- 拟议的GWRF方法适用于区域范围的滑坡易感性评估.
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