Machine Learning Feature Selection Methods for Landslide Susceptibility Mapping

Details

Serval ID
serval:BIB_582759E1A1BD
Type
Article: article from journal or magazin.
Collection
Publications
Title
Machine Learning Feature Selection Methods for Landslide Susceptibility Mapping
Journal
Mathematical Geosciences
Author(s)
Micheletti  Natan, Foresti  Loris, Robert  Sylvain, Leuenberger  Michael, Pedrazzini  Andrea, Jaboyedoff  Michel, Kanevski  Mikhail
ISSN
1874-8961 (Print)
1874-8953 (Electronic)
Publication state
Published
Issued date
12/2013
Peer-reviewed
Oui
Volume
46
Number
1
Pages
33-57
Language
english
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.
Create date
01/04/2015 17:30
Last modification date
20/08/2019 15:12
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