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首页> 外文期刊>Geomorphology >Landslide susceptibility assessment using logistic regression and its comparison with a rock mass classification system, along a road section in the northern Himalayas (India)
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Landslide susceptibility assessment using logistic regression and its comparison with a rock mass classification system, along a road section in the northern Himalayas (India)

机译:使用Logistic回归进行滑坡敏感性评估,并将其与岩体分类系统进行比较,沿喜马拉雅山北部地区(印度)

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摘要

Landslide studies are commonly guided by ground knowledge and field measurements of rock strength and slope failure criteria. With increasing sophistication of GIS-based statistical methods, however, landslide susceptibility studies benefit from the integration of data collected from various sources and methods at different scales. This study presents a logistic regression method for landslide susceptibility mapping and verifies the result by comparing it with the geotechnical-based slope stability probability classification (SSPC) methodology. The study was carried out in a landslide-prone national highway road section in the northern Himalayas, India. Logistic regression model performance was assessed by the receiver operator characteristics (ROC) curve, showing an area under the curve equal to 0.83. Field validation of the SSPC results showed a correspondence of 72% between the high and very high susceptibility classes with present landslide occurrences. A spatial comparison of the two susceptibility maps revealed the significance of the geotechnical-based SSPC method as 90% of the area classified as high and very high susceptible zones by the logistic regression method corresponds to the high and very high class in the SSPC method. On the other hand, only 34% of the area classified as high and very high by the SSPC method falls in the high and very high classes of the logistic regression method. The underestimation by the logistic regression method can be attributed to the generalisation made by the statistical methods, so that a number of slopes existing in critical equilibrium condition might not be classified as high or very high susceptible zones.
机译:滑坡研究通常以地面知识以及岩石强度和边坡破坏准则的现场测量为指导。但是,随着基于GIS的统计方法的日益完善,滑坡敏感性研究得益于从各种规模和不同来源收集的数据的整合。这项研究提出了一种用于滑坡敏感性图的逻辑回归方法,并将其与基于岩土工程的边坡稳定性概率分类(SSPC)方法进行了比较,以验证结果。这项研究是在印度喜马拉雅山北部易发生滑坡的国家高速公路路段进行的。通过接收者操作员特征(ROC)曲线评估Logistic回归模型的性能,该曲线下面积等于0.83。 SSPC结果的现场验证显示,在高和非常高的磁化率等级与当前滑坡发生之间的对应率为72%。对两个磁化率图的空间比较显示,基于岩土技术的SSPC方法具有重要意义,因为逻辑回归方法将90%的区域划分为高易感区和高易感区,对应于SSPC方法中的高和高等级。另一方面,被SSPC方法分类为高和非常高的区域中只有34%属于逻辑回归方法的高和非常高的类别。逻辑回归方法的低估可以归因于统计方法的概括,因此在临界平衡条件下存在的许多坡度可能不会被归类为高或非常高的易感区。

著录项

  • 来源
    《Geomorphology》 |2010年第4期|627-637|共11页
  • 作者单位

    International Institute for Geo-Information Science and Earth Observation (ITC), 7500 AA, Enschede, The Netherlands Indian Institute of Remote Sensing, 4-Kalidas Road, Dehradun, India;

    Indian Institute of Remote Sensing, 4-Kalidas Road, Dehradun, India;

    International Institute for Geo-Information Science and Earth Observation (ITC), 7500 AA, Enschede, The Netherlands;

    International Institute for Geo-Information Science and Earth Observation (ITC), 7500 AA, Enschede, The Netherlands;

    International Institute for Geo-Information Science and Earth Observation (ITC), 7500 AA, Enschede, The Netherlands;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    landslide susceptibility; slope stability; logistic regression; SSPC; GIS;

    机译:滑坡敏感性边坡稳定性逻辑回归SSPC;地理信息系统;

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