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Landslide Susceptibility Assessment at the Basin Scale for Rainfall- and Earthquake-Triggered Shallow Slides

机译：流域规模的降雨和地震触发的浅层滑坡敏感性评估

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摘要

The hydrographic basin of Ribeira Grande (S. Miguel Island, Azores) has a set of characteristics that enhance the occurrence of shallow slides that have been triggered by rainfall and earthquakes. Two landslide inventories were built according to the landslide triggers: Landslide Inventory 2 (LI 2), which includes 174 earthquake-triggered shallow slides occurred in 2005; and Landslide Inventory 1 (LI 1), which includes 442 shallow slides triggered by rainfall in several periods from 2005 to 2016. Both landslide inventories were characterized and compared from the morphometric point of view and were used individually to produce susceptibility models to failure using a simple bivariate state-of-the-art statistical method (the Information Value). The landslide susceptibility Models were validated using success rates, prediction rates, and Kappa statistics. The results show that shallow slides triggered by rainfall and earthquakes in the study area have different morphometric characteristics. It was verified that models produced with LI 1 are very effective in predicting the spatial location of LI 2, but the same does not happen in the inverse situation. Finally, landslide susceptibility models developed with LI 1 and LI 2 for the upper sector of the hydrographic basin (where most landslides occurred), and latter applied to the complete watershed, present more modest predictive results but are more reliable to characterize the landslide susceptibility in the study area.
机译：Ribeira Grande（亚速尔群岛圣米格尔岛）的水文盆地具有一系列特征，可增强降雨和地震引发的浅​​层滑坡的发生。根据滑坡触发因素，建立了两个滑坡清单：滑坡清单2（LI 2），其中包括2005年发生的174次地震触发的浅层滑坡;滑坡清单1（LI 1），其中包括442个由2005年至2016年的降雨引发的浅层滑坡。从形态计量学角度对这两个滑坡清单进行了特征描述和比较，并分别用于建立对滑坡的敏感性模型。简单的双变量最新统计方法（信息价值）。使用成功率，预测率和Kappa统计数据验证了滑坡敏感性模型。结果表明，研究区降雨和地震触发的浅层滑坡具有不同的形态特征。可以证明，使用LI 1生成的模型在预测LI 2的空间位置方面非常有效，但是在相反情况下不会发生相同的情况。最后，利用LI 1和LI 2为水文盆地上段（发生了大部分滑坡）开发了滑坡敏感性模型，并将其应用于完整的流域，其预测结果较为温和，但对于表征滑坡敏感性的描述更为可靠。学习区。

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