Mapping earthquake-triggered landslide susceptibility by use of artificial neural network (ANN) models: an example of the 2013 Minxian (China) Mw 5.9 event-Reference-Cited by-同舟云学术

Mapping earthquake-triggered landslide susceptibility by use of artificial neural network (ANN) models: an example of the 2013 Minxian (China) Mw 5.9 event

Published:2018-12-25 Issue:1 Volume:10 Page:1-25
ISSN:1947-5705
Container-title:Geomatics, Natural Hazards and Risk
language:en
Short-container-title:Geomatics, Natural Hazards and Risk

Author:

Tian Yingying¹^ORCID,Xu Chong¹^ORCID,Hong Haoyuan²³^ORCID,Zhou Qing¹,Wang Duo¹^ORCID

Affiliation:

1. Key Laboratory of Active Tectonics and Volcano, Institute of Geology, China Earthquake Administration, Beijing, China;

2. College of Geographic Sciences, Nanjing Normal University, Nanjing, China;

3. Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Scientific Research Fund of Institute of Engineering Mechanics, China Earthquake Administration

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences,General Environmental Science

Link

https://tandfonline.com/doi/pdf/10.1080/19475705.2018.1487471

Reference67 articles.

1. Susceptibility assessments of the Wenchuan earthquake-triggered landslides in Longnan using logistic regression

2. A GIS-based comparative study of Dempster-Shafer, logistic regression and artificial neural network models for landslide susceptibility mapping

3. GIS-based landslide susceptibility modelling: a comparative assessment of kernel logistic regression, Naïve-Bayes tree, and alternating decision tree models

4. Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong

5. Spatial distribution of landslides triggered by the 2008 Ms 8.0 Wenchuan earthquake, China

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