滑坡敏感性模型有效性的对比分析

Jordan Henrique de Souza, Gislaine dos Santos, Gabriela Guimarães Gouvêa de Oliveira, Raphaella de Souza Resende Moreira, Clarice Simões Monnerat
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引用次数: 0

摘要

本研究旨在分析SHALSTAB、IPT、SAGA、SMORPH和修正SMORPH模型在滑坡易感性评价中的有效性。统计分析了各模型在滑坡危险性预测中的有效性,以及各模型在发生或不发生滑坡情况下的一致性。在这项工作中,对1996年至2012年巴西Juiz de Fora市报告的15,544例病例进行了logistic回归、受试者工作特征(ROC)曲线、Kappa统计和一致性分析。该分析包括855个已确认的滑坡和14689个未确认的滑坡。通过对历史发生压舱率的分析发现,需要添加敏感性分析模型中所包含的新变量。在SHALSTAB、IPT、SAGA、SMORPH和修正SMORPH模型显示滑坡可能性较低的许多情况下,发生了许多意义重大的滑坡,其中包括人员伤亡。本研究的重要性在于通过新的补充变量的指示来评估这些模型的有效性。结果表明,与自然条件相比,具有相似岩土力学特征的边坡会受到不同的要求,因此人为变量是必要的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparative Analysis of the Efficacy of Landslide Susceptibility Models
This research aims to analyze the efficacy of SHALSTAB, IPT, SAGA, SMORPH and modified SMORPH models in evaluating landslide susceptibility. Statistical analysis concerning both the efficacy of the models in the prediction of landslide risks and the concordance between the models according to landslide occurrence or non-occurrence was performed. For this work, logistic regression, Receiver Operating Characteristic (ROC) curves, Kappa statistic and concordance analysis were used considering a sample of 15,544 incidents reported during the period of 1996 to 2012 in the city of Juiz de Fora, Brazil. The analysis included 855 confirmed landslide occurrences and 14,689 unconfirmed occurrences. The need for the addition of new variables other than those included in the susceptibility analysis models was observed by the analysis of the historical ballast of occurrence. In many cases where SHALSTAB, IPT, SAGA, SMORPH and modified SMORPH models pointed to a low possibility of a landslide, many landslides of great significance occurred, which included casualties. The importance of this study is to assess the efficacy of these models through the indication of new complementary variables. The results show that an anthropogenic variable is necessary as slopes with similar geotechnical characteristics are submitted to different demands compared to natural conditions.
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