Preliminary analyses of a catastrophic landslide occurred on July 23, 2019, in Guizhou Province, China
Preliminary analyses of a catastrophic landslide
We gratefully acknowledge the financial support of the National Key R&D Program of China (2017YFC1501102), the National Natural Science Foundation of China (51579163, 51639007, and 41977229), and the Fundamental Scientific Research Fund in the IEF, CEA (2019IEF0201), CAS Pioneer Hundred Talents Program and JSPS Program.
- 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
- 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 CrossRefGoogle Scholar
- 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