Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA
Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA
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DOI：
10.1016/j.geomorph.2009.10.002
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发表时间：
2010-02
期刊：
影响因子：
3.9
通讯作者：
N. Regmi;J. Giardino;J. Vitek
中科院分区：
文献类型：
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作者：
N. Regmi;J. Giardino;J. Vitek
科罗拉多州的保尼亚-麦克卢尔山口地区以活跃的群众运动而闻名。我们研究了该地区735个活跃的浅层运动特征，包括泥石流、泥石流、岩石滑坡和土壤滑坡。危险区域的识别是灾害管理的基本组成部分，也是促进滑坡易发地区人类安全居住和基础设施发展的重要基础。基于证据权重（WOE）的贝叶斯定理被用来绘制可能危险的山体滑坡地图。利用地理信息系统（GIS）和统计软件包完成建模。使用WOE方法测量和加权导致滑坡的17个因素，以创建易发生滑坡的地区的地图。在进行卡方检验以确定相互条件独立的因素后，对加权因子的图逐像素求和。通过结合地形、水文、地质、土地覆盖和人类影响等因素，开发了六个模型。通过观测到的滑坡分布来评价各模型的性能。最好的地图的有效性是根据滑坡来检验的，而滑坡并没有被纳入分析。绘制的易受滑坡影响地区地图的预测精度为78%。
The Paonia–McClure Pass area of Colorado is well known for active mass movements. We examined 735 active shallow movement features, including debris flows, debris slides, rock slides and soil slides, in this area. Identification of the hazardous areas is a fundamental component of disaster management and an important basis for promoting safe human occupation and infrastructure development in landslide prone areas. Bayes' theorem, based on the weight of evidence (WOE), was used to create a map of landslides that could be hazardous. The modeling was accomplished by employing a geographical information system (GIS) and a statistical package. Seventeen factors that cause landslides were measured and weighted using the WOE method to create a map of areas susceptible to landslides. The maps of weighted factors were summed on a pixel-by-pixel basis after performing chi-square tests to determine factors that are conditionally independent of each other. By combining factors that represent topography, hydrology, geology, land cover, and human influences, six models were developed. The performance of each model was evaluated by the distribution of the observed landslides. The validity of the best map was checked against landslides, which were not entered in the analysis. The resulting map of areas susceptible to landslides has a prediction accuracy of 78%.