Correlation does not imply geomorphic causation in data-driven landslide susceptibility modelling – Benefits of exploring landslide data collection effects

Steger, Stefan, Mair, Volkmar, Kofler, Christian, Pittore, Massimiliano, Zebisch, Marc and Schneiderbauer, Stefan, (2021). Correlation does not imply geomorphic causation in data-driven landslide susceptibility modelling – Benefits of exploring landslide data collection effects. Science of the Total Environment, 776(145935), 1-16

Document type:
Article
Collection:
UNU-EHS Articles

Metadata
Documents
Links
Versions
Statistics
  • Attached Files (Some files may be inaccessible until you login with your UNU Collections credentials)
    Name Description MIMEType Size Downloads
    StegerEtAl_2021_Landslide-data-collection-effects_META.pdf StegerEtAl_2021_Landslide-data-collection-effects_META.pdf application/pdf 5.43MB
  • Sub-type Journal article
    Author Steger, Stefan
    Mair, Volkmar
    Kofler, Christian
    Pittore, Massimiliano
    Zebisch, Marc
    Schneiderbauer, Stefan
    Title Correlation does not imply geomorphic causation in data-driven landslide susceptibility modelling – Benefits of exploring landslide data collection effects
    Appearing in Science of the Total Environment
    Volume 776
    Issue No. 145935
    Publication Date 2021-02-18
    Place of Publication Amsterdam
    Publisher Elsevier B.V.
    Start page 1
    End page 16
    Language eng
    Abstract Data-driven landslide susceptibility models formally integrate spatial landslide information with explanatory environmental variables that describe predisposing factors of slope instability. Well-performing models are commonly utilized to identify landslide-prone terrain or to understand the causes of slope instability. In most cases, however, the available landslide data is affected by spatial biases (e.g. under representation of landslides far from infrastructure or in forests) and does therefore not perfectly represent the spatial distribution of past slope instabilities. Literature shows that implications of such data flaws are frequently ignored. This study was built upon landslide information that systematically relates to damage-causing and infrastructure-threatening events in South Tyrol, Italy (7400 km2). The created models represent three conceptually different strategies to deal with biased landslide information. The aims were to demonstrate why an inference of geomorphic causation from apparently well-performing models is invalid under common landslide data bias conditions (Model 1), to test a novel bias-adjustment approach (Model 2) and to exploit the underlying data bias to model areas likely affected by potentially damaging landslides (Model 3; intervention index), instead of landslide susceptibility. The study offers a novel perspective on how biases in landslide data can be considered within data-driven models by focusing not only on the process under investigation (landsliding), but also on the circumstances that led to the registration of landslide information (data collection effects). The results were evaluated in terms of statistical relationships, variable importance, predictive performance, and geomorphic plausibility.
    Keyword Generalized additive model
    Landslide inventory
    Validation
    South Tyrol
    Landslide exposure
    Copyright Holder The Authors
    Copyright Year 2021
    Copyright type Creative commons
    ISBN 00489697
    DOI 10.1016/j.scitotenv.2021.145935
  • Versions
    Version Filter Type
  • Citation counts
    Google Scholar Search Google Scholar
    Access Statistics: 594 Abstract Views, 332 File Downloads  -  Detailed Statistics
    Created: Mon, 08 Mar 2021, 23:11:21 JST by Aarti Basnyat on behalf of UNU EHS