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Erschienen in:
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2019 | OriginalPaper | Buchkapitel

13. Machine Learning Techniques in Landslide Susceptibility Mapping: A Survey and a Case Study

verfasst von : Taskin Kavzoglu, Ismail Colkesen, Emrehan Kutlug Sahin

Erschienen in: Landslides: Theory, Practice and Modelling

Verlag: Springer International Publishing

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Abstract

Machine learning techniques have been increasingly employed for solving many scientific and engineering problems. These data driven methods have been lately utilized with great success to produce landslide susceptibility maps. They give promising results particularly for mapping large landslide prone areas with limited geotechnical data. This chapter surveys their use in landslide susceptibility analysis and presents a case study investigating their effectiveness with regard to a conventional statistical method, namely logistic regression. It starts with the importance of spatial prediction of future landslides from past and present ones and discusses the requirement of advanced techniques for landslide susceptibility mapping. A critical literature survey is given under five main categories including core algorithms and their ensembles together with their hybrid forms. An application is presented for machine learning application using bagging, random forest, rotation forest and support vector machines with their optimal settings.

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Vorheriges Kapitel Integration of Terrestrial Laser Scanning and GIS Analysis for Multi-temporal Landslide Monitoring: A Case Study of the Mont de La Saxe (Aosta Valley, NW Italy)
Nächstes Kapitel Landslides in Permafrost Zone of Russia
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Metadaten
Titel
Machine Learning Techniques in Landslide Susceptibility Mapping: A Survey and a Case Study
verfasst von
Taskin Kavzoglu
Ismail Colkesen
Emrehan Kutlug Sahin
Copyright-Jahr
2019
Buch
Landslides: Theory, Practice and Modelling

Print ISBN: 978-3-319-77376-6

Electronic ISBN: 978-3-319-77377-3

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-319-77377-3_13