Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchment, Turkey
Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchment, Turkey
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DOI：
10.1016/s0013-7952(03)00143-1
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发表时间：
2004-02
影响因子：
7.4
通讯作者：
M. L. Süzen;V. Doyuran
中科院分区：
文献类型：
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作者：
M. L. Süzen;V. Doyuran
近几十年来，滑坡灾害危险性评价引起了众多学者的关注。提出了一些参数来定量地解释滑坡的机理，其中许多参数是非常重要和真实的。但是，某些数据类型和模型是特定于站点的，无法应用于不同的位置。此外，存储在连续参数图中的数据被任意地划分为多个类，这取决于专家的眼光。基本上，该划分控制了双变量分析的结果。此外，控制机构的参数映射的负责部分也被任意加权。基于这两个事实，类边界对生成的易感性/危险图产生了偏见，这导致依赖于用户的知识，而不是依赖于数据和事实本身。本研究的目的是在更依赖数据的趋势中完善先前定义的方法。为了实现这一目标，两个新的概念：种子细胞和百分位数地图。种子细胞是被认为是代表最好的原状形态决策规则（滑坡发生前的条件）的区域，并将通过在滑坡的冠部和侧翼区域添加缓冲区来实现。为了对输入参数图进行定量分类，参数图中的种子单元的数据分布基于其分布的百分位断点被分成多个类，参数图直接依赖于种子单元分布，因此依赖于数据本身。
In the last decades, landslide hazard assessment has attracted many researchers' attention. A number of parameters are suggested to be responsible to quantitatively explain the mechanism of landslides; many of these parameters are very important and factual. However, some data types and models are site-specific and could not be applied to different locations. Furthermore, the data stored in continuous parameter maps are divided into a number of classes arbitrarily, depending on the vision of the expert. Basically, this division controls the result of bivariate analysis. Besides, the responsible portion of the parameter map controlling the mechanism is also weighted arbitrarily. Based on these two facts, the class boundaries put a prejudice on the produced susceptibility/hazard maps, which result in dependence on the knowledge of the user rather than being dependent on the data and the fact itself. The aim of this study is to refine the previously defined methods in a more data-dependent trend. To achieve this goal, two new concepts: seed cells and percentile maps are introduced. Seed cells are the zones that are considered to represent the best undisturbed morphological decision rules (conditions before landslide occurs) and would be achieved by adding a buffer zone to the crown and flank areas of the landslide. To quantitatively classify the input parameter maps, the data distributions of seed cells in the parameter maps are divided into a number of classes on the basis of their distribution's percentile break-points upon which the parameter maps are directly dependent on the seed cell distributions, hence to the data itself.