An extreme rainfall-induced landslide susceptibility assessment using autoencoder combined with random forest in Shimane Prefecture, Japan

Background Landslide-affecting factors are uncorrelated or non-linearly correlated, limiting the predictive performance of traditional machine learning methods for landslide susceptibility assessment. Deep learning methods can take advantage of the high-level representation and reconstruction of inf...

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Veröffentlicht in:	Geoenvironmental disasters Jg. 7; H. 1; S. 1 - 16
Hauptverfasser:	Nam, Kounghoon, Wang, Fawu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 30.01.2020 Springer Nature B.V SpringerOpen
Schlagworte:	Algorithms Classifiers Deep learning Earth and Environmental Science Earth Sciences Environment Environmental Science and Engineering Extreme weather Feature extraction Geoecology/Natural Processes Geography Landslide susceptibility Landslides Learning algorithms Machine learning Modelling of fast landslides Natural Hazards Performance prediction Rain Rainfall Random forest Sparse autoencoder Stacked autoencoder Support vector machine Support vector machines Japan Stacked autoencoder Random forest Sparse autoencoder Support vector machine Landslide susceptibility
ISSN:	2197-8670, 2197-8670
Online-Zugang:	Volltext
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