基于SMOTE-Tomek采样和机器学习算法的山体滑坡易感性评估
Ming-Zhou Lv1, Kun-Lun Li2, Jia-Zeng Cai2
1School of Civil Engineering and Architecture, Zhejiang Sci-Tech University, Hangzhou, China.
PloS one
|May 21, 2025
概括
这项研究开发了一种精确的机器学习框架,以评估中国泰平镇的山体滑坡易感性. 随机森林模型确定了砍伐斜坡的高度作为影响滑坡风险的主要因素.
科学领域:
- 地质科学 地质科学
- 地质工程是地质工程.
- 环境科学 环境科学
背景情况:
- 滑坡是影响安全和基础设施的重大地质危险.
- 目前的滑坡易感性评估往往缺乏足够的数据,并依赖于主观经验.
- 准确的评估对于有效的风险管理和缓解策略至关重要.
研究的目的:
- 开发和验证一个强大的框架,以评估乡镇规模的山体滑坡易感性.
- 为了比较多个机器学习模型的性能,以绘制山体滑坡易感性的地图.
- 通过使用可解释的人工智能来识别滑坡发生的关键影响因素.
主要方法:
- 利用了1325个斜率单位的数据集,在中国泰平镇有9个特征.
- 应用数据平衡技术,包括合成少数群体过量采样技术和托马克链接 (SMOTE-托马克).
- 将六种机器学习模型进行比较,并使用夏普利增量扩展 (SHAP) 进行因子分析.
主要成果:
- 随机森林 (RF) 模型表现出最佳准确性 (0.791) 和F1得分 (0.723).
- 确定了非常低,低,中和高灵敏度区域,分别覆盖92.27%,5.12%,1.78%和0.83%的面积.
- 确定切割斜坡的高度是最重要的因素,而高度的影响较小.
结论:
- 拟议的机器学习框架准确地评估了乡镇规模的山体滑坡易感性.
- 这些发现为有针对性的风险管理和缓解努力提供了有价值的数据.
- 强调在滑坡预测中考虑特定的地形学因素的重要性,例如切割斜坡高度.
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