Machine Learning Feature Selection Methods for Landslide Susceptibility Mapping

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ID: 261659
2013
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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.
Reference Key
micheletti2013mathematicalmachine Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Natan Micheletti;Loris Foresti;Sylvain Robert;Michael Leuenberger;Andrea Pedrazzini;Michel Jaboyedoff;Mikhail Kanevski;Natan Micheletti;Loris Foresti;Sylvain Robert;Michael Leuenberger;Andrea Pedrazzini;Michel Jaboyedoff;Mikhail Kanevski;
Journal mathematical geosciences
Year 2013
DOI
doi:10.1007/s11004-013-9511-0
URL
https://dx.doi.org/10.1007/s11004-013-9511-0
Keywords
hydrogeology physics earth sciences statistics for engineering computer science chemistry and earth sciences general geotechnical engineering & applied earth sciences

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