提高基于ML的山体滑坡易感性,使用组合方法进行样本选择:印度希马查尔邦康格拉地区的案例研究
Ankit Singh1, Nitesh Dhiman1, Niraj K C2
1DExtER Lab, School of Civil and Environmental Engineering, North Campus, IIT Mandi, A-11 Building, Mandi, 175075, Himachal Pradesh, India.
Environmental science and pollution research international
|September 2, 2024
概括
有效的山体滑坡易感性测绘 (LSM) 使用合并方法. 从非常高和非常低易感区域分别采样滑坡和非滑坡点,大大提高了预测的准确性.
科学领域:
- 地质科学 地质科学
- 环境科学 环境科学
- 地质技术工程 地质技术工程
背景情况:
- 开发有效的山体滑坡易感性地图 (LSM) 对于降低风险至关重要.
- 集成方法显示了提高LSM预测准确性的潜力,特别是在有限的数据的情况下.
- 机器学习,特别是分类,从整体策略中获益.
研究的目的:
- 调查整体策略,以改善山体滑坡易感性预测.
- 评估采样滑坡和非滑坡点对预测准确性的影响.
- 使用不同的采样策略,比较随机森林 (RF) 和支持矢量机 (SVM) 的性能.
主要方法:
- 用不同的采样策略准备了三组数据集,用于康格拉地区的山体滑坡和非山体滑坡点.
- 数据集1:最初的滑坡和随机的非滑坡点.
- 数据集2和3:结合了非常容易发生山体滑坡的区域和各种非山体滑坡采样,使用RF和SVM算法.
主要成果:
- 最高的曲线下的面积 (AUC) 值是通过数据集3 (从非常高和非常低的敏感区域采样) 实现的,达到0.952 (SVM) 和0.954 (RF).
- 数据集3还为SVM和RF提供了最高的精度和回忆值.
- 仅从非常敏感的区域 (数据集2) 取样,导致性能较差和错误分类错误.
结论:
- 组合方法,特别是从非常高和非常低易感区域分别采样滑坡和非滑坡点,显著提高了LSM的预测能力.
- 这种方法导致了更精确的山体滑坡区划分,有助于危险管理和决策.
- 该研究验证了拟议的整体策略对准确地评估山体滑坡易感性的有效性.
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