机器学习方法分析了江河流盆地的云南部分的碎片流感
Jingyi Zhou1, Jiangcheng Huang2, Zhengbao Sun3
1School of Earth Sciences, Yunnan University, Kunming, China.
PeerJ
|May 24, 2024
概括
随机森林模型准确地预测了中国努江河流域的碎片流动易感性. 地形,地质和人类活动是影响碎片流发生的关键因素.
科学领域:
- 地质科学 地质科学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 努江河流域 (YNR) 的云南部分是中国碎片流的高风险区域.
- 阿尔卑斯山谷地区在碎片流量评估方面存在独特的挑战.
研究的目的:
- 评估三种机器学习算法的有效性,用于碎片流感受性建模.
- 确定控制YNR盆地碎片流发生的主要因素.
主要方法:
- 应用随机森林 (RF),线性支向量机 (Linear SVM) 和辐射基函数支向量机 (RBFSVM) 算法.
- 分析了20个潜在的影响因素,包括地形,地质,降水,植被和人为因素.
主要成果:
- 与RBFSVM和线性SVM相比,RF显示出更高的精度.
- 地形条件是先决条件,相对高度差异是最重要的因素.
- 地质,降水,植被和人类活动极大地影响了碎片流的形成.
结论:
- 射频模型为YNR盆地提供可靠的碎片流感受性 (DFS) 地图.
- 结果提供了方法指南,以加强在类似的高山山谷环境中对DFS的评估.
- 结果有助于更深入地了解复杂的山区碎片流动动态.
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