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Machine Learning Feature Selection Methods for Landslide Susceptibility Mapping

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Abstract

This paper explores the use of adaptive support vector machines, random forests and AdaBoost for landslide susceptibility mapping in three separated regions of Canton Vaud, Switzerland, based on a set of geological, hydrological and morphological features. The feature selection properties of the three algorithms are studied to analyze the relevance of features in controlling the spatial distribution of landslides. The elimination of irrelevant features gives simpler, lower dimensional models while keeping the classification performance high. An object-based sampling procedure is considered to reduce the spatial autocorrelation of data and to estimate more reliably generalization skills when applying the model to predict the occurrence of new unknown landslides. The accuracy of the models, the relevance of features and the quality of landslide susceptibility maps were found to be high in the regions characterized by shallow landslides and low in the ones with deep-seated landslides. Despite providing similar skill, random forests and AdaBoost were found to be more efficient in performing feature selection than adaptive support vector machines. The results of this study reveal the strengths of the classification algorithms, but evidence: (1) the need for relying on more than one method for the identification of relevant variables; (2) the weakness of the adaptive scaling algorithm when used with landslide data; and (3) the lack of additional features which characterize the spatial distribution of deep-seated landslides.

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Acknowledgments

This study was partially funded by the Swiss National Science Foundation projects Geokernels: kernel-based methods for geo- and environmental sciences. Phase II (No. 200020-121835/1) and rockslides in Rhône valley (No. 200021-118105). We thank Prof. Stuart Lane for the interesting comments provided and Pierrick Nicolet for his valuable help. We also are grateful to the two anonymous reviewers, who provided us with constructive comments and helped in improving the quality of the manuscript.

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Authors and Affiliations

  1. Institute of Earth Surface Dynamics, Faculty of Geosciences and Environment, University of Lausanne, 1015 , Lausanne, Switzerland

    Natan Micheletti, Michael Leuenberger & Mikhail Kanevski

  2. Royal Meteorological Institute of Belgium, 1180 , Brussels, Belgium

    Loris Foresti

  3. Seminar for Statistics, Swiss Federal Institute of Technology, 8092 , Zürich, Switzerland

    Sylvain Robert

  4. Institute of Earth Sciences, Faculty of Geosciences and Environment, University of Lausanne, 1015 , Lausanne, Switzerland

    Andrea Pedrazzini & Michel Jaboyedoff

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  1. Natan Micheletti
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  2. Loris Foresti
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Correspondence to Natan Micheletti.

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Micheletti, N., Foresti, L., Robert, S. et al. Machine Learning Feature Selection Methods for Landslide Susceptibility Mapping. Math Geosci 46, 33–57 (2014). https://doi.org/10.1007/s11004-013-9511-0

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