Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling

Autor(en)

J. N. Goetz, A. Brenning, H. Petschko, P. Leopold

Abstrakt

Statistical and now machine learning prediction methods have been gaining popularity in the field of landslide susceptibility modeling. Particularly, these data driven approaches show promise when tackling the challenge of mapping landslide prone areas for large regions, which may not have sufficient geotechnical data to conduct physically-based methods. Currently, there is no best method for empirical susceptibility modeling. Therefore, this study presents a comparison of traditional statistical and novel machine learning models applied for regional scale landslide susceptibility modeling. These methods were evaluated by spatial k-fold cross-validation estimation of the predictive performance, assessment of variable importance for gaining insights into model behavior and by the appearance of the prediction (i.e. susceptibility) map. The modeling techniques applied were logistic regression (GLM), generalized additive models (GAM), weights of evidence (WOE), the support vector machine (SVM), random forest classification (RF), and bootstrap aggregated classification trees (bundling) with penalized discriminant analysis (BPLDA). These modeling methods were tested for three areas in the province of Lower Austria, Austria. The areas are characterized by different geological and morphological settings.Random forest and bundling classification techniques had the overall best predictive performances. However, the performances of all modeling techniques were for the majority not significantly different from each other; depending on the areas of interest, the overall median estimated area under the receiver operating characteristic curve (AUROC) differences ranged from 2.9 to 8.9 percentage points. The overall median estimated true positive rate (TPR) measured at a 10% false positive rate (FPR) differences ranged from 11 to 15pp. The relative importance of each predictor was generally different between the modeling methods. However, slope angle, surface roughness and plan curvature were consistently highly ranked variables. The prediction methods that create splits in the predictors (RF, BPLDA and WOE) resulted in heterogeneous prediction maps full of spatial artifacts. In contrast, the GAM, GLM and SVM produced smooth prediction surfaces. Overall, it is suggested that the framework of this model evaluation approach can be applied to assist in selection of a suitable landslide susceptibility modeling technique.

Organisation(en)

Institut für Geographie und Regionalforschung

Externe Organisation(en)

Austrian Institute of Technology, University of Waterloo (UW), Friedrich-Schiller-Universität Jena

Journal

Computers & Geosciences

Band

81

Seiten

1-11

Anzahl der Seiten

11

ISSN

0098-3004

DOI

https://doi.org/10.1016/j.cageo.2015.04.007

Publikationsdatum

08-2015

Peer-reviewed

Ja

ÖFOS 2012

105404 Geomorphologie

Schlagwörter

ASJC Scopus Sachgebiete

Computers in Earth Sciences, Information systems

Link zum Portal

https://ucrisportal.univie.ac.at/de/publications/evaluating-machine-learning-and-statistical-prediction-techniques-for-landslide-susceptibility-modeling(f5d3f8a1-8ef6-4462-aeb0-76bd1539cdaa).html