How can statistical models help to determine driving factors of landslides?

Peter Vorpahl, Helmut Elsenbeer, Michael Märker and Boris Schröder

Ecological Modelling, 2012, vol. 239, issue C, 27-39

Abstract: Landslides are a hazard for humans and artificial structures. From an ecological point of view, they represent an important ecosystem disturbance, especially in tropical montane forests. Here, shallow translational landslides are a frequent natural phenomenon and one local determinant of high levels of biodiversity. In this paper, we apply weighted ensembles of advanced phenomenological models from statistics and machine learning to analyze the driving factors of natural landslides in a tropical montane forest in South Ecuador. We exclusively interpret terrain attributes, derived from a digital elevation model, as proxies to several driving factors of landslides and use them as predictors in our models which are trained on a set of five historical landslide inventories. We check the model generality by transferring them in time and use three common performance criteria (i.e. AUC, explained deviance and slope of model calibration curve) to, on the one hand, compare several state-of-the-art model approaches and on the other hand, to create weighted model ensembles. Our results suggest that it is important to consider more than one single performance criterion.

Keywords: Landslides; Tropical montane forests; Statistical modeling; Model comparison; Artificial neuronal network; Classification trees; Random forests; Boosted regression trees; Generalized linear models; Multivariate adaptive regression splines; Maximum entropy method; Weighted model ensembles (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:239:y:2012:i:c:p:27-39

DOI: 10.1016/j.ecolmodel.2011.12.007

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