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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 14, issue 1
Nat. Hazards Earth Syst. Sci., 14, 95–118, 2014
https://doi.org/10.5194/nhess-14-95-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Progress in landslide hazard and risk evaluation

Nat. Hazards Earth Syst. Sci., 14, 95–118, 2014
https://doi.org/10.5194/nhess-14-95-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 16 Jan 2014

Research article | 16 Jan 2014

Assessing the quality of landslide susceptibility maps – case study Lower Austria

H. Petschko1, A. Brenning2, R. Bell1, J. Goetz1,2, and T. Glade1 H. Petschko et al.
  • 1Department of Geography and Regional Research, University of Vienna, Austria
  • 2Department of Geography and Environmental Management, University of Waterloo, Ontario N2L 3G1, Canada

Abstract. Landslide susceptibility maps are helpful tools to identify areas potentially prone to future landslide occurrence. As more and more national and provincial authorities demand for these maps to be computed and implemented in spatial planning strategies, several aspects of the quality of the landslide susceptibility model and the resulting classified map are of high interest. In this study of landslides in Lower Austria, we focus on the model form uncertainty to assess the quality of a flexible statistical modelling technique, the generalized additive model (GAM). The study area (15 850 km2) is divided into 16 modelling domains based on lithology classes. A model representing the entire study area is constructed by combining these models. The performances of the models are assessed using repeated k-fold cross-validation with spatial and random subsampling. This reflects the variability of performance estimates arising from sampling variation. Measures of spatial transferability and thematic consistency are applied to empirically assess model quality. We also analyse and visualize the implications of spatially varying prediction uncertainties regarding the susceptibility map classes by taking into account the confidence intervals of model predictions. The 95% confidence limits fall within the same susceptibility class in 85% of the study area. Overall, this study contributes to advancing open communication and assessment of model quality related to statistical landslide susceptibility models.

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