用多个网格分辨率的机器学习模型对福古镇山体滑坡易感性的比较评估
Shuhang Ni1, Yang Xie2,3
1Xi'an University of Science and Technology College of Geology and Environment, Xi'an, 710048, China. 920422462@qq.com.
Scientific reports
|December 13, 2025
概括
在中国优化山体滑坡易感模型.
科学领域:
- 地质科学 地质科学
- 环境科学 环境科学
- 地球表面的过程 地球表面的过程
背景情况:
- 在中国的低谷地区,山体滑坡存在重大风险.
- 由于相互作用的因素,预测山体滑坡易感性是复杂的.
- 之前对空间分辨率和机器学习模型的研究产生了不一致的结果.
研究的目的:
- 确定机器学习模型和网格分辨率的最佳组合,以评估山体滑坡易感性.
- 为了评估空间尺度对在洛斯丘陵地区的模型性能的影响.
- 为管理山体滑坡风险提供实用参考.
主要方法:
- 使用随机森林 (RF) 和XGBoost (XGB) 模型对滑坡易感性的比较分析.
- 在15米,30米和60米的电网分辨率上进行多尺度评估. 在西省福古镇.
- 评估预测准确度和易感区的空间模式.
主要成果:
- 网格分辨率显著影响预测准确性和易感区分布.
- 15米分辨率的XGBoost模型显示了最高的性能 (AUC = 0.980).
- 高和非常高的易感区主要在北部和中西部峡谷山地形中确定.
结论:
- 最佳的模型分辨率组合对于准确地绘制山体滑坡易感性地图至关重要.
- 空间规模是影响机器学习模型在Loess区域的性能的一个关键因素.
- 结果为山西北部的山体滑坡风险管理提供了方法指导和实际见解.
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