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首页> 外文期刊>Bulletin of engineering geology and the environment >Application of a GIS-based slope unit method for landslide susceptibility mapping along the rapidly uplifting section of the upper Jinsha River, South-Western China
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Application of a GIS-based slope unit method for landslide susceptibility mapping along the rapidly uplifting section of the upper Jinsha River, South-Western China

机译：基于GIS的坡度单元法在中国西南金沙江上游快速抬升段滑坡敏感性图中的应用。

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The objective of this study was to produce a landslide susceptibility map along the rapidly uplifting section of the upper Jinsha River. Firstly, a total of 40 landslides were identified in the study area from the interpretation of remote sensing (RS) and field survey data. Following landslide identification, ten variables including slope angle, slope aspect, curvature, land use, normalised difference vegetation index (NDVI), rainfall, lithology, distance to river, distance to fault, and Strahler's integral value were selected as the influencing factors in landslide susceptibility mapping. All of the influencing factors were extracted by the slope unit. The Strahler's integral value was used to represent the relationship between the rate of uplift and rate of denudation in each slope unit. Furthermore, three methods, including logistic regression, a support vector machine, and an artificial neural network, were applied to landslide susceptibility modelling. Five-fold cross validation, a statistical analysis method, and the area under the receiver operating characteristic curve (AUC) were used to compare the evaluation results of the three models. Finally, the variance-based method was used to find the key factors associated with landslides in the study area. The results show that the mean prediction accuracies of the logistic regression model, artificial neural network model, and support vector machine model were 80.47%, 87.30%, and 83.94% in the training stage, respectively, and 81.08%, 82.16%, and 83.51% in the validating stage, respectively. The mean AUCs of the three models were 88.16%, 93.96%, and 89.68% in the training stage, respectively, and 87.68%, 92.60%, and 89.88% in the validating stage, respectively. These results show that the artificial neural network model is the best model for evaluating landslide susceptibility in this study. The landslide susceptibility map produced by the artificial neural network model was divided into five classes, including very low, low, moderate, high, and very high, and the percentages of the areas of the five susceptibility classes were 17.23%, 28.32%, 22.73%, 16.73%, and 15.00%, respectively. Furthermore, the distance to river, slope aspect, lithology, and distance to fault are the most important influencing factors for landslide susceptibility mapping in the study area. Consequently, this study will be a useful guide for landslide prevention, mitigation, and future land planning in the study area.
机译：这项研究的目的是在金沙江上游急速上升段绘制滑坡敏感性图。首先，根据遥感的解释和现场调查数据，在研究区域总共发现了40个滑坡。在确定滑坡之后，选择了十个变量，包括坡度角，坡度，曲率，土地利用，归一化植被指数（NDVI），降雨，岩性，到河流的距离，到断层的距离以及Strahler积分值作为影响滑坡的因素。敏感性映射。所有影响因素均由斜率单元提取。 Strahler的积分值用于表示每个坡度单位中抬升速率与剥蚀速率之间的关系。此外，将逻辑回归，支持向量机和人工神经网络这三种方法应用于滑坡敏感性模型。使用五重交叉验证，统计分析方法和接收器工作特性曲线（AUC）下的面积来比较这三个模型的评估结果。最后，使用基于方差的方法来查找与研究区域中的滑坡相关的关键因素。结果表明，在训练阶段，逻辑回归模型，人工神经网络模型和支持向量机模型的平均预测准确度分别为80.47％，87.30％和83.94％，分别为81.08％，82.16％和83.51。 ％分别在验证阶段。三种模型在训练阶段的平均AUC分别为88.16％，93.96％和89.68％，在验证阶段分别为87.68％，92.60％和89.88％。这些结果表明，人工神经网络模型是评估滑坡敏感性的最佳模型。人工神经网络模型产生的滑坡敏感性图分为极低，极低，中，高，极高五类，这五类的面积百分比分别为17.23％，28.32％，22.73。 ％，16.73％和15.00％。此外，到河的距离，坡度，岩性和到断层的距离是研究区域滑坡敏感性图绘制的最重要影响因素。因此，这项研究将为研究地区的滑坡预防，减缓和未来土地规划提供有用的指导。

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