A comparative evaluation of machine learning algorithms and an improved optimal model for landslide susceptibility: a case study

A comparative evaluation of machine learning algorithms and an improved optimal model for landslide susceptibility: a case study

In this study, four representative machine learning methods (support vector machine (SVM), maximum entropy (MaxEnt), random forest (RF), and artificial neural network (ANN)) were employed to construct a landslide susceptibility map (LSM) in Xulong Gully (XLG), southwest China. The models were subseq...

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Bibliographic Details
Main Authors: Yue Liu, Peihua Xu, Chen Cao, Bo Shan, Kuanxing Zhu, Qiuyang Ma, Zongshuo Zhang, Han Yin
Format: Article
Language:English
Published: Taylor & Francis Group 2021-01-01
Series:Geomatics, Natural Hazards & Risk
Subjects:
landslide susceptibility
support vector machine
maximum entropy
random forest
artificial neural network
xulong gully
Online Access:http://dx.doi.org/10.1080/19475705.2021.1955018

Internet

http://dx.doi.org/10.1080/19475705.2021.1955018

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