A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling-Reference-Cited by-同舟云学术

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A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling

Published:2018-04-17 Issue:4 Volume:78 Page:2865-2886
ISSN:1435-9529
Container-title:Bulletin of Engineering Geology and the Environment
language:en
Short-container-title:Bull Eng Geol Environ

Author:

Pham Binh Thai,Jaafari Abolfazl,Prakash Indra,Bui Dieu Tien

Publisher

Springer Science and Business Media LLC

Subject

Geology,Geotechnical Engineering and Engineering Geology

Link

http://link.springer.com/content/pdf/10.1007/s10064-018-1281-y.pdf

Reference92 articles.

1. Abdulwahid WM, Pradhan B (2016) Landslide vulnerability and risk assessment for multi-hazard scenarios using airborne laser scanning data (LiDAR) Landslides:1–20

2. Althuwaynee OF, Pradhan B, Park H-J, Lee JH (2014) A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. Catena 114:21–36

3. Antronico L, Borrelli L, Coscarelli R, Gullà G (2015) Time evolution of landslide damages to buildings: the case study of Lungro (Calabria, southern Italy). Bull Eng Geol Environ 74:47–59

4. Antronico L, Borrelli L, Coscarelli R, Pasqua A, Petrucci O, Gullà G (2013) Slope movements induced by rainfalls damaging an urban area: the Catanzaro case study (Calabria, southern Italy). Landslides 10:801–814

5. Araújo MB, New M (2007) Ensemble forecasting of species distributions trends in ecology & evolution 22:42-47

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