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

). 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. The results revealed that none of the models reflected landslide susceptibility. Despite partly high predictive performances, the models were unable to create geomorphically plausible spatial predictions. The impact-oriented intervention index, however, enabled to identify damage-causing landslides with high accuracy. We conclude that the frequent practice of inferring geomorphic causation from well-performing models without accounting for data limitations is invalid.