Articles | Volume 17, issue 7
https://doi.org/10.5194/nhess-17-1091-2017
https://doi.org/10.5194/nhess-17-1091-2017
Research article
 | 
10 Jul 2017
Research article |  | 10 Jul 2017

Combination of statistical and physically based methods to assess shallow slide susceptibility at the basin scale

Sérgio C. Oliveira, José L. Zêzere, Sara Lajas, and Raquel Melo

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Cited articles

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Bui, D. T., Tuan, T. A., Klempe, H., Pradhan, B., and Revhaug, I.: Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree, Landslides, 13, 361–378, https://doi.org/10.1007/s10346-015-0557-6, 2016.
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Short summary
Approaches to assess shallow slide susceptibility at the basin scale are conceptually different depending on the use of statistical or physically based methods. Two hypotheses are tested: (i) both methods generate similar shallow slide susceptibility results and (ii) the combination of both susceptibility maps generates a more reliable susceptibility model. Model combinations registered a higher predictive capacity and the identification of areas where the results from both models are uncertain.
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