The Challenge of “Trivial Areas” in Statistical Landslide Susceptibility Modelling
- Author(s)
- Stefan Steger, Thomas Glade, Matjaz Mikos, Binod Tiwari, Yueping Yin, Kyoji Sassa
- Abstract
Landslide susceptibility maps are frequently produced by fitting multiple variable statistical models that generate a relationship between a binary response variable (presence and absence of past landslides) and a set of predisposing environmental factors. Within this study, we investigated the hypothesis that an inclusion of a high portion of “trivial areas” (e.g. flat areas) affects modelled relationships, quantitative validation results and the appearance of the final maps. This assumption was tested by systematically comparing logistic regression models that were based on data sets which ignored respectively included a high portion of “trivial areas”. Modelled relationships were evaluated by estimating odds ratios for all predictors. The Area under the Receiver Operating Characteristic Curve (AUROC) provided information on the prediction skill of each model. This performance measure was assessed by applying non-spatial and spatial partitioning techniques. Each analysis was additionally performed with artificial samples to confirm our observations. The results showed that the delineation of the study area affected modelled relationships and consequently the spatial pattern of landslide susceptibility maps as well. AUROC values confirmed that the apparent prediction skill of a model may increase whenever a high portion of easily classifiable areas (e.g. flat area) is included. Therefore we concluded that an interpretation of modelled relationships and prediction skills should always consider the spatial extent to which the respective statistical landslide susceptibility analysis was carried out. The apparent prediction performance of a geomorphic meaningless model can be enhanced by including a high portion of easily classifiable areas.
- Organisation(s)
- Department of Geography and Regional Research
- Pages
- 803-808
- No. of pages
- 6
- Publication date
- 2017
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 105408 Physical geography
- Portal url
- https://ucrisportal.univie.ac.at/en/publications/261cce0b-3543-4146-8a35-da294c403bd8