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首页> 外文期刊>Catena: An Interdisciplinary Journal of Soil Science Hydrology-Geomorphology Focusing on Geoecology and Landscape Evolution >Investigation of the influence of nonoccurrence sampling on landslide susceptibility assessment using Artificial Neural Networks
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Investigation of the influence of nonoccurrence sampling on landslide susceptibility assessment using Artificial Neural Networks

机译：使用人工神经网络调查非电流抽样对滑坡敏感性评估的影响

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Landslide susceptibility assessment using Artificial Neural Networks (ANNs) requires occurrence (landslide) and nonoccurrence (not prone to landslide) samples for ANN training. We present empirical evidence that a priori intervention on the nonoccurrence samples can produce models that are improper for generalization. Thirteen nonoccurrence cases based on GIS data from Rolante River basin (828.26 km(2)) in Brazil are studied, divided in three groups. The first group was based on six combinations of buffers with different minimum and maximum distances from the mapped scars (BO). The second group (RO) acquired nonoccurrence only from a rectangle in the lowlands, known for not being susceptible to landslides. For BR, six alternatives respectively to BO were presented, with the inclusion of nonoccurrence samples acquired from the same rectangle used for RO. Accuracy (acc) and the Area Under Receiving Operating Characteristic Curve (AUC) were calculated. RO resulted in perfect discrimination between susceptible and not susceptible to landslides (acc = 1 e AUC = 1). This occurred because the model simply provided susceptible classification to points in which attributes are different from those in the rectangle, harming the classification of nonoccurrence sampling points outside the rectangle. RO map shows large areas classified as susceptible which are known to be non-susceptible. In BR, sampling points from the rectangle, which are easy to classify, were added to the verification sample of BR. Average acc for BO 00 m (minimum buffer distance to scars of 0 m): 89.45%, average acc for BR 00 m: 92.33%, average AUC for BO 00 m: 0.9409, average AUC for BR 00 m: 0.9616. Maps of groups BO and BR were alike. This indicates that metrics can be artificially risen if biased samples are added, although the final map is not visibly affected. To avoid this effect, the employment of easily classifiable samples, generated based on expert knowledge, should be made carefully.
机译：使用人工神经网络（ANN）进行滑坡敏感性评估需要发生（滑坡）和未发生（不易发生滑坡）样本进行ANN训练。我们提供了经验证据，证明对未发生样本的先验干预可能会产生不适合推广的模型。根据巴西罗兰特河流域（828.26 km（2））的GIS数据，研究了13个未发生病例，分为三组。第一组基于六种缓冲区组合，它们与映射疤痕（BO）的最小和最大距离不同。第二组（RO）仅从低地的一个矩形中获得不发生，以不易发生滑坡而闻名。对于BR，分别给出了六种BO替代方案，包括从用于RO的同一矩形中采集的未出现样本。计算准确度（acc）和接收工作特性曲线下面积（AUC）。RO完美区分了易受滑坡影响和不易受滑坡影响（acc=1 e AUC=1）。这是因为该模型只是为属性与矩形中不同的点提供了易受影响的分类，从而损害了矩形外未出现采样点的分类。RO地图显示了被归类为易感区域的大片区域，这些区域已知为不易感区域。在BR中，将矩形中易于分类的采样点添加到BR的验证样本中。BO 00 m的平均acc（到0 m疤痕的最小缓冲距离）：89.45%，BR 00 m的平均acc:92.33%，BO 00 m的平均AUC:0.9409，BR 00 m的平均AUC:0.9616。BO组和BR组的地图很相似。这表明，如果添加有偏差的样本，可以人为地提高度量，尽管最终的地图没有明显的影响。为了避免这种影响，应谨慎使用基于专家知识生成的易于分类的样本。

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