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Volume 16, issue 12 | Copyright
Nat. Hazards Earth Syst. Sci., 16, 2729-2745, 2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 16 Dec 2016

Research article | 16 Dec 2016

The propagation of inventory-based positional errors into statistical landslide susceptibility models

Stefan Steger1, Alexander Brenning2, Rainer Bell1, and Thomas Glade1 Stefan Steger et al.
  • 1Department of Geography and Regional Research, University of Vienna, Universitätsstraße 7, 1010 Vienna, Austria
  • 2Department of Geography, Friedrich Schiller University Jena, Loebdergraben 32, 07743 Jena, Germany

Abstract. There is unanimous agreement that a precise spatial representation of past landslide occurrences is a prerequisite to produce high quality statistical landslide susceptibility models. Even though perfectly accurate landslide inventories rarely exist, investigations of how landslide inventory-based errors propagate into subsequent statistical landslide susceptibility models are scarce.

The main objective of this research was to systematically examine whether and how inventory-based positional inaccuracies of different magnitudes influence modelled relationships, validation results, variable importance and the visual appearance of landslide susceptibility maps. The study was conducted for a landslide-prone site located in the districts of Amstetten and Waidhofen an der Ybbs, eastern Austria, where an earth-slide point inventory was available.

The methodological approach comprised an artificial introduction of inventory-based positional errors into the present landslide data set and an in-depth evaluation of subsequent modelling results. Positional errors were introduced by artificially changing the original landslide position by a mean distance of 5, 10, 20, 50 and 120m. The resulting differently precise response variables were separately used to train logistic regression models. Odds ratios of predictor variables provided insights into modelled relationships. Cross-validation and spatial cross-validation enabled an assessment of predictive performances and permutation-based variable importance. All analyses were additionally carried out with synthetically generated data sets to further verify the findings under rather controlled conditions.

The results revealed that an increasing positional inventory-based error was generally related to increasing distortions of modelling and validation results. However, the findings also highlighted that interdependencies between inventory-based spatial inaccuracies and statistical landslide susceptibility models are complex. The systematic comparisons of 12 models provided valuable evidence that the respective error-propagation was not only determined by the degree of positional inaccuracy inherent in the landslide data, but also by the spatial representation of landslides and the environment, landslide magnitude, the characteristics of the study area, the selected classification method and an interplay of predictors within multiple variable models. Based on the results, we deduced that a direct propagation of minor to moderate inventory-based positional errors into modelling results can be partly counteracted by adapting the modelling design (e.g. generalization of input data, opting for strongly generalizing classifiers). Since positional errors within landslide inventories are common and subsequent modelling and validation results are likely to be distorted, the potential existence of inventory-based positional inaccuracies should always be considered when assessing landslide susceptibility by means of empirical models.

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Short summary
This study investigates the propagation of landslide inventory-based positional errors into statistical landslide susceptibility models by artificially introducing such spatial inaccuracies. The findings highlight that (i) an increasing positional error is related to increasing distortions of modelling and validation results, (ii) interrelations between inventory-based errors and modelling results are complex, and (iii) inventory-based errors can be counteracted by adapting the study design.
This study investigates the propagation of landslide inventory-based positional errors into...