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Methodology for Landslide Susceptibility Mapping by Means of a GIS. Application to the Contraviesa Area (Granada, Spain)

Author

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  • T. Fernández
  • C. Irigaray
  • R. El Hamdouni
  • J. Chacón

Abstract

This article presents a method to map landslide susceptibility in rock massifs using Geographical Information Systems (GIS). The method is based on making an inventory of rupture zones of different types of slope movements and then analysing the bivariate correlation of these with the factors that determine instability. After determining the factors that present the highest correlation with each type of movement, a matrix is created to combine these factors and to determine the percentage of the rupture zone in each combination, which provides an expression of the susceptibility of the terrain. The map thus obtained is divided into susceptibility classes. The susceptibility maps (made in 1995) for each type of movement are first calibrated with the inventory of the movements from which they are derived (previous to 1995), and subsequently validated by another inventory elaborated after the susceptibility maps (in 1997). In both cases, significant correlation coefficients were obtained (the Goodman–Kruskal coefficients were over 0.8 and sometimes exceeded 0.9). The relative error (degree of accumulated fit for very low to low susceptibility classes) was always less than 5%,while the relative success rate was always above 50%. These resultsillustrate the adequacy of the method and of the maps obtained. Copyright Kluwer Academic Publishers 2003

Suggested Citation

  • T. Fernández & C. Irigaray & R. El Hamdouni & J. Chacón, 2003. "Methodology for Landslide Susceptibility Mapping by Means of a GIS. Application to the Contraviesa Area (Granada, Spain)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 30(3), pages 297-308, November.
  • Handle: RePEc:spr:nathaz:v:30:y:2003:i:3:p:297-308
    DOI: 10.1023/B:NHAZ.0000007092.51910.3f
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    Citations

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    Cited by:

    1. José Antonio Palenzuela & Jorge David Jiménez-Perálvarez & José Chacón & Clemente Irigaray, 2016. "Assessing critical rainfall thresholds for landslide triggering by generating additional information from a reduced database: an approach with examples from the Betic Cordillera (Spain)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(1), pages 185-212, October.
    2. Yang Hong & Robert Adler & George Huffman, 2007. "Use of satellite remote sensing data in the mapping of global landslide susceptibility," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 43(2), pages 245-256, November.
    3. S. Boussouf & T. Fernández & A. B. Hart, 2023. "Landslide susceptibility mapping using maximum entropy (MaxEnt) and geographically weighted logistic regression (GWLR) models in the Río Aguas catchment (Almería, SE Spain)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(1), pages 207-235, May.
    4. Vahed Ghiasi & Seyed Amir Reza Ghasemi & Mahyar Yousefi, 2021. "Landslide susceptibility mapping through continuous fuzzification and geometric average multi-criteria decision-making approaches," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(1), pages 795-808, May.
    5. A. Clerici & S. Perego & C. Tellini & P. Vescovi, 2010. "Landslide failure and runout susceptibility in the upper T. Ceno valley (Northern Apennines, Italy)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 52(1), pages 1-29, January.
    6. Massimo Conforti & Gaetano Robustelli & Francesco Muto & Salvatore Critelli, 2012. "Application and validation of bivariate GIS-based landslide susceptibility assessment for the Vitravo river catchment (Calabria, south Italy)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 61(1), pages 127-141, March.
    7. Adel Ghasemi & Omid Bahmani & Samira Akhavan & Hamid Reza Pourghasemi, 2023. "Investigation of land-subsidence phenomenon and aquifer vulnerability using machine models and GIS technique," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(2), pages 1645-1671, September.
    8. Guilherme Garcia Oliveira & Luis Fernando Chimelo Ruiz & Laurindo Antonio Guasselli & Claus Haetinger, 2019. "Random forest and artificial neural networks in landslide susceptibility modeling: a case study of the Fão River Basin, Southern Brazil," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 99(2), pages 1049-1073, November.

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