Machine Learning Feature Selection Methods for Landslide Susceptibility Mapping

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 thr...

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Veröffentlicht in:	Mathematical geosciences 2014-01, Vol.46 (1), p.33-57
Hauptverfasser:	Micheletti, Natan, Foresti, Loris, Robert, Sylvain, Leuenberger, Michael, Pedrazzini, Andrea, Jaboyedoff, Michel, Kanevski, Mikhail
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Artificial intelligence Chemistry and Earth Sciences Classification Computer Science Earth and Environmental Science Earth Sciences Geotechnical Engineering & Applied Earth Sciences Hydrogeology Landslides Landslides & mudslides Machine learning Mapping Mathematical analysis Mathematical models Physics Risk assessment Spatial distribution Statistics for Engineering Support vector machines
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