Research Article Open Access

Comparative Analysis of the Efficacy of Landslide Susceptibility Models

Jordan Henrique de Souza1, Gislaine dos Santos1, Gabriela Guimarães Gouvêa de Oliveira1, Raphaella de Souza Resende Moreira1 and Clarice Simões Monnerat1
  • 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.

American Journal of Environmental Sciences
Volume 15 No. 3, 2019, 90-106

DOI: https://doi.org/10.3844/ajessp.2019.90.106

Submitted On: 1 January 2019 Published On: 31 May 2019

How to Cite: de Souza, J. H., dos Santos, G., Gouvêa de Oliveira, G. G., Resende Moreira, R. S. & Monnerat, C. S. (2019). Comparative Analysis of the Efficacy of Landslide Susceptibility Models. American Journal of Environmental Sciences, 15(3), 90-106. https://doi.org/10.3844/ajessp.2019.90.106

  • 5,122 Views
  • 2,781 Downloads
  • 0 Citations

Download

Keywords

  • Landslide
  • Susceptibility Maps
  • Predictive Models
  • Efficacy