Debris-flow susceptibility assessment using the characteristic factors of a catchment
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摘要: 文章以云南省迪庆藏族自治州金沙江河谷金江镇—林当可为例，研究了基于子流域单元的泥石流易发性评价模型。首先，将研究区划分为1 570个子流域单元，其中70%(1 090个)用于模型建立，30%(480个)作为模型测试样本。然后，采用9种子流域单元特征因子（melton比率、流域平均径流侵蚀力指数、平均地形湿度指数、流域延伸率、流域平均高程、流域水系密度、流域平均植被覆盖度、流域平均坡度、流域高差率）的归一化值作为自变量，泥石流是否发育作为因变量，运用逻辑回归建立泥石流易发性评价模型，将易发性划分为5个等级。最后，通过独立的测试样本验证，易发性评价模型预测性能(AUC=0.821)良好；且随着易发性等级提高，泥石流绝对和相对发育比例逐级增加，模型满足合理性要求，适用于研究区的泥石流易发性评价。Abstract: In traditional application of GIS, the susceptibility mapping based on distinct grids usually neglect the organic unity of the natural element. For this reason, this article investigates the susceptibility model based on catchments, using the valleys of the Jinsha River between Jinjiang and Lindangke as a working example. Firstly, the study area is divided into 1 570 catchments, in which 70% (1 090 catchments) are used to build the susceptibility model and 30% (480 catchments) are used as validation samples. Secondly, 9 feature factors, including the melton ratio, average stream power index of catchment, average topographic wetness index of catchment, catchment elongation, average height of catchment, drainage density of catchment, average fractional vegetation cover of catchment, average slope of catchment and catchment relief ratio are normalized and then selected as independent variables. Whether debris-flows occur is the dependent variable. Susceptibility assessment models of debris-flow are then built using the logistic regression and are classified into five degrees. Finally, the model can predict well (AUC=0.821) through the test of independent validation samples. Moreover, both the absolute and relative incidence of debris-flows increase with the susceptibility degree.
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