A comparative study of landslide susceptibility mapping using weight of evidence, logistic regression and support vector machine and evaluated by SBAS-InSAR monitoring: Zhouqu to Wudu segment in Bailong River Basin, China-Reference-Cited by-同舟云学术

A comparative study of landslide susceptibility mapping using weight of evidence, logistic regression and support vector machine and evaluated by SBAS-InSAR monitoring: Zhouqu to Wudu segment in Bailong River Basin, China

Published:2017-04 Issue:8 Volume:76 Page:
ISSN:1866-6280
Container-title:Environmental Earth Sciences
language:en
Short-container-title:Environ Earth Sci

Author:

Xie Zhengtuan,Chen Guan,Meng Xingmin,Zhang Yi,Qiao Liang,Tan Long

Funder

Ministry of Education of the People's Republic of China

Ministry of Science and Technology of the People's Republic of China

State Key Laboratory of Geohazard Prevention and Geoenvironment Protection of Chengdu University of Technology

Publisher

Springer Science and Business Media LLC

Subject

Earth-Surface Processes,Geology,Pollution,Soil Science,Water Science and Technology,Environmental Chemistry,Global and Planetary Change

Link

http://link.springer.com/article/10.1007/s12665-017-6640-7/fulltext.html

Reference63 articles.

1. Akgun A (2012) A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey. Landslides 9(1):93–106

2. Allison PD (2001) Logistic regression using SAS System: theory and application. Wiley Interscience, New York

3. Arora M, Das Gupta A, Gupta R (2004) An artificial neural network approach for landslide hazard zonation in the Bhagirathi (Ganga) Valley, Himalayas. Int J Remote Sens 25(3):559–572

4. Atkinson P, Massari R (1998) Generalised linear modelling of susceptibility to landsliding in the central Apennines, Italy. Comput Geosci 24(4):373–385

5. Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda–Yahiko Mountains, Central Japan. Geomorphology 65(1):15–31

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