Comparative Analysis of the Efficacy of Landslide Susceptibility Models
- 1 UFJF Federal University of Juiz de Fora, Brazil
Abstract
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.
DOI: https://doi.org/10.3844/ajessp.2019.90.106
Copyright: © 2019 Jordan Henrique de Souza, Gislaine dos Santos, Gabriela Guimarães Gouvêa de Oliveira, Raphaella de Souza Resende Moreira and Clarice Simões Monnerat. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- 4,944 Views
- 2,647 Downloads
- 0 Citations
Download
Keywords
- Landslide
- Susceptibility Maps
- Predictive Models
- Efficacy