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Comparing landslide susceptibility models in the Río El Estado watershed on the SW flank of Pico de Orizaba volcano, Mexico

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  • Gabriel Legorreta Paulín
  • Solène Pouget
  • Marcus Bursik
  • Fernando Aceves Quesada
  • Trevor Contreras

Abstract

A comprehensive study of landslide susceptibility models is carried out in the Río El Estado watershed on the SW flank of Pico de Orizaba volcano. A detailed multitemporal landslide inventory map in the watershed is used as a framework for the quantitative comparison of three landslide susceptibility models. The first landslide susceptibility map is created by using the Stability Index MAPping model. The second and the third landslide susceptibility maps are created using multiple logistic regression (MLR) and multicriteria evaluation models. The validation of the resulting susceptibility maps is performed by comparing them with an inventory map in a contingency table and through the area under the receiver operating characteristic curve. The results point out that the models tend to over-predict and have a moderate to high match with the landslide areas. In this research, MLR is preferred over the other two models because MLR obtains similar or better results with fewer significant variables. Copyright Springer Science+Business Media Dordrecht 2016

Suggested Citation

  • Gabriel Legorreta Paulín & Solène Pouget & Marcus Bursik & Fernando Aceves Quesada & Trevor Contreras, 2016. "Comparing landslide susceptibility models in the Río El Estado watershed on the SW flank of Pico de Orizaba volcano, Mexico," 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. 80(1), pages 127-139, January.
    • Handle: RePEc:spr:nathaz:v:80:y:2016:i:1:p:127-139
    DOI: 10.1007/s11069-015-1960-y
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    References listed on IDEAS

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    1. Gabriel Legorreta Paulín & Marcus Bursik & José Hubp & Luis Mejía & Fernando Aceves Quesada, 2014. "A GIS method for landslide inventory and susceptibility mapping in the Río El Estado watershed, Pico de Orizaba volcano, México," 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. 71(1), pages 229-241, March.
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    1. Rutilio Castro-Miguel & Gabriel Legorreta-Paulín & Roberto Bonifaz-Alfonzo & José Fernando Aceves-Quesada & Miguel Ángel Castillo-Santiago, 2022. "Modeling spatial landslide susceptibility in volcanic terrains through continuous neighborhood spatial analysis and multiple logistic regression in La Ciénega watershed, Nevado de Toluca, Mexico," 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. 113(1), pages 767-788, August.
    2. Darya Golovko & Sigrid Roessner & Robert Behling & Birgit Kleinschmit, 2017. "Automated derivation and spatio-temporal analysis of landslide properties in southern Kyrgyzstan," 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. 85(3), pages 1461-1488, February.
    3. Jean Baptiste Nsengiyumva & Geping Luo & Egide Hakorimana & Richard Mind'je & Aboubakar Gasirabo & Valentine Mukanyandwi, 2019. "Comparative Analysis of Deterministic and Semiquantitative Approaches for Shallow Landslide Risk Modeling in Rwanda," Risk Analysis, John Wiley & Sons, vol. 39(11), pages 2576-2595, November.

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