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Landslide susceptibility mapping using maximum entropy (MaxEnt) and geographically weighted logistic regression (GWLR) models in the Río Aguas catchment (Almería, SE Spain)

Author

Listed:
  • S. Boussouf

    (Government Sub-Delegation in Almería)

  • T. Fernández

    (University of Jaén
    University of Jaén)

  • A. B. Hart

    (Atkins Ltd)

Abstract

A landslide susceptibility analysis has been made in the Río Aguas catchment (Almeria, Southeast Spain), using two statistical models, Maximum Entropy (MaxEnt) and Geographically Weighted Logistic Regression (GWLR). For this purpose, a previous landslide inventory has been used and re-elaborated, reaching a total incidence of 2.58% of the whole area. Different types of movements have been distinguished, being rock falls, slides and complex movements the predominant. From the inventory, the centroid of the rupture zone has been extracted to represent the landslides introduced in the models. A previous factor analysis has been made, using 12 predictors related to morphometry, hydrography, geology and land cover, with 5 m grid spacing, allowing the selection of factors to be used in the analysis and discarding those showing correlation between them. Then, MaxEnt and GWLR models are applied using different distributions of training and testing samples from the landslide inventory. For the validation, the Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC) has been used but additionally, the degree of fit (DF) has allowed to validate the rupture zones themselves, not only the centroids. Results show an excellent prediction with both metrics in all the methods and samples, but the better results are obtained in the GWLR method for AUC and in the MaxEnt for the degree of fit. Therefore, a consensus model of both methods has been obtained, that improves even more the results reaching an AUC value of 0.99 and a degree of fit of 90%.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:nathaz:v:117:y:2023:i:1:d:10.1007_s11069-023-05857-7
    DOI: 10.1007/s11069-023-05857-7
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    References listed on IDEAS

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    1. 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.
    2. Vorpahl, Peter & Elsenbeer, Helmut & Märker, Michael & Schröder, Boris, 2012. "How can statistical models help to determine driving factors of landslides?," Ecological Modelling, Elsevier, vol. 239(C), pages 27-39.
    3. C. Irigaray & T. Fernández & R. El Hamdouni & J. Chacón, 2007. "Evaluation and validation of landslide-susceptibility maps obtained by a GIS matrix method: examples from the Betic Cordillera (southern 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. 41(1), pages 61-79, April.
    4. Ian W. Renner & David I. Warton, 2013. "Equivalence of MAXENT and Poisson Point Process Models for Species Distribution Modeling in Ecology," Biometrics, The International Biometric Society, vol. 69(1), pages 274-281, March.
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