The influence of systematically incomplete shallow landslide inventories on statistical susceptibility models and suggestions for improvements

Autor(en)

S. Steger, A. Brenning, R. Bell, T. Glade

Abstrakt

Complete landslide inventories are rarely available. The objectives of this study were to (i) elaborate the influence of incomplete landslide inventories on statistical landslide susceptibility models and to (ii) propose suitable modelling strategies that can reduce the effects of inventory-based incompleteness. In this context, we examined whether the application of a novel statistical approach, namely mixed-effects models, enables predictions that are less influenced by such inventory-based errors. The study was conducted for (i) an area located in eastern Austria and (ii) a synthetically generated data set. The applied methodology consisted of a simulation of two different inventory-based biases and an in-depth evaluation of subsequent modelling results. Inventory-based errors were simulated by gradually removing landslide data within forests and selected municipalities. The resulting differently biased inventories were introduced into logistic regression models while we considered the effects of including or excluding predictors that are directly related to the respective inventory-based bias. Mixed-effects logistic regression was used to account for variation that was due to an inventory-based incompleteness. The results show that most erroneous predictions, but highest predictive performances, were obtained from models generated with highly incomplete inventories and predictors that were able to directly describe the respective incompleteness. An exclusion of such bias-describing predictors led to systematically confounded relationships. The application of mixed-effects models proved valuable to produce predictions that were least affected by inventory-based errors. This paper highlights that the degree of inventory-based incompleteness is only one of several aspects that determine how an inventory-based bias may propagate into the final results. We propose a four-step procedure to deal with incomplete inventories in the context of statistical landslide susceptibility modelling.

Organisation(en)

Institut für Geographie und Regionalforschung

Externe Organisation(en)

Friedrich-Schiller-Universität Jena

Journal

Landslides

Band

14

Seiten

1767–1781

Anzahl der Seiten

15

ISSN

1612-510X

DOI

https://doi.org/10.1007/s10346-017-0820-0

Publikationsdatum

10-2017

Peer-reviewed

Ja

ÖFOS 2012

105404 Geomorphologie

Schlagwörter

ASJC Scopus Sachgebiete

Geotechnical Engineering and Engineering Geology

Link zum Portal

https://ucrisportal.univie.ac.at/de/publications/19a23553-ee75-44d7-946a-6d6b96afeb65