Data-driven ensemble model for probabilistic prediction of debris-flow volume using Bayesian model averaging-Reference-Cited by-同舟云学术

Data-driven ensemble model for probabilistic prediction of debris-flow volume using Bayesian model averaging

Published:2023-01 Issue:1 Volume:82 Page:
ISSN:1435-9529
Container-title:Bulletin of Engineering Geology and the Environment
language:en
Short-container-title:Bull Eng Geol Environ

Author:

Tian Mi,Fan Hao,Xiong Zimin,Li Lihua

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hubei Province

Wuhan Knowledge Innovation Special Project

Publisher

Springer Science and Business Media LLC

Subject

Geology,Geotechnical Engineering and Engineering Geology

Link

https://link.springer.com/content/pdf/10.1007/s10064-022-03050-x.pdf

Reference50 articles.

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2. Bianco G, Franzi L (2000) Estimation of debris flow volumes from storm events. Second International Congress on Debris Flows Hazard Mitigation, Taipei, Taiwan, 441–448

3. Bishop CM (2006) Pattern recognition and machine learning. Springer, Singapore

4. Bovis MJ, Jakob M (1999) The role of debris supply to determine debris flow activity. Earth Surf Proc Land 24:1039–1054. https://doi.org/10.1002/(SICI)1096-9837(199910)24:11%3c1039::AID-ESP29%3e3.0.CO;2-U

5. Breiman L (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324

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