Preliminary analyses of a catastrophic landslide occurred on July 23, 2019, in Guizhou Province, China
Author:
Publisher
Springer Science and Business Media LLC
Subject
Geotechnical Engineering and Engineering Geology
Link
http://link.springer.com/content/pdf/10.1007/s10346-019-01334-0.pdf
Reference7 articles.
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2. Dou J, Yamagishi H, Pourghasemi HR et al (2015) An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan. Nat Hazards 78:1749–1776. https://doi.org/10.1007/s11069-015-1799-2
3. Dou J, Yunus AP, Bui DT, Merghadi A, Sahana M, Zhu Z, Chen CW, Han Z, Pham BT (2019a) Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed. Japan Landslides:1–18. https://doi.org/10.1007/s10346-019-01286-5
4. Dou J, Yunus AP, Tien Bui D, Merghadi A, Sahana M, Zhu Z, Chen CW, Khosravi K, Yang Y, Pham BT (2019b) Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan. Sci Total Environ 662:332–346. https://doi.org/10.1016/j.scitotenv.2019.01.221
5. Pham BT, Prakash I, Dou J et al (2019) A novel hybrid approach of landslide susceptibility modelling using rotation forest ensemble and different base classifiers. Geocarto Int. https://doi.org/10.1080/10106049.2018.1559885
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