A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses
A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses
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DOI：
10.1016/j.enggeo.2009.10.001
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发表时间：
2010-01
影响因子：
7.4
通讯作者：
A. Nandi;A. Shakoor
中科院分区：
文献类型：
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作者：
A. Nandi;A. Shakoor
采用双变量和多变量统计分析方法对美国俄亥俄州东北部凯霍加河流域滑坡的空间分布进行了预测，并利用地理信息系统（GIS）对滑坡发生与各种不稳定因素之间的关系进行了评价。利用从航空照片、实地检查和现有文献中确定的滑坡位置，编制了滑坡清单地图。将导致滑坡发生的坡角、土壤类型、土壤可蚀性、土壤流动性指数、地表覆盖格局、降水、靠近河流等失稳因子以栅格数据层的形式导入ArcGIS中，并采用与区域物理条件相对应的数值尺度进行排序。为了研究各不稳定因素对滑坡空间分布的控制作用，采用双变量和多变量模型对数字数据集进行了分析。多元模型分析采用logistic回归方法。这两种模型都有助于生成滑坡敏感性图，并通过曲线下面积法和将图与已知滑坡位置进行比较来评估每种模型的适用性。多元logistic回归模型是预测该地区滑坡易感性的较好模型。逻辑回归模型生成了1:24 000比例尺的滑坡易感性图，将易感性分为低、中、高和非常高四类。结果还表明，坡角、靠近河流、土壤可蚀性和土壤类型在控制边坡运动方面具有统计学意义。
Bivariate and multivariate statistical analyses were used to predict the spatial distribution of landslides in the Cuyahoga River watershed, northeastern Ohio, U.S.A. The relationship between landslides and various instability factors contributing to their occurrence was evaluated using a Geographic Information System (GIS) based investigation. A landslide inventory map was prepared using landslide locations identified from aerial photographs, field checks, and existing literature. Instability factors such as slope angle, soil type, soil erodibility, soil liquidity index, landcover pattern, precipitation, and proximity to stream, responsible for the occurrence of landslides, were imported as raster data layers in ArcGIS, and ranked using a numerical scale corresponding to the physical conditions of the region. In order to investigate the role of each instability factor in controlling the spatial distribution of landslides, both bivariate and multivariate models were used to analyze the digital dataset. The logistic regression approach was used in the multivariate model analysis. Both models helped produce landslide susceptibility maps and the suitability of each model was evaluated by the area under the curve method, and by comparing the maps with the known landslide locations. The multivariate logistic regression model was found to be the better model in predicting landslide susceptibility of this area. The logistic regression model produced a landslide susceptibility map at a scale of 1:24,000 that classified susceptibility into four categories: low, moderate, high, and very high. The results also indicated that slope angle, proximity to stream, soil erodibility, and soil type were statistically significant in controlling the slope movement.