Predictive Performances of Ensemble Machine Learning Algorithms in Landslide Susceptibility Mapping Using Random Forest, Extreme Gradient Boosting (XGBoost) and Natural Gradient Boosting (NGBoost)-Reference-Cited by-同舟云学术

Predictive Performances of Ensemble Machine Learning Algorithms in Landslide Susceptibility Mapping Using Random Forest, Extreme Gradient Boosting (XGBoost) and Natural Gradient Boosting (NGBoost)

Published:2022-01-17 Issue:6 Volume:47 Page:7367-7385
ISSN:2193-567X
Container-title:Arabian Journal for Science and Engineering
language:en
Short-container-title:Arab J Sci Eng

Author:

Kavzoglu Taskin^ORCID,Teke Alihan

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Link

https://link.springer.com/content/pdf/10.1007/s13369-022-06560-8.pdf

Reference83 articles.

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3. Safaei, M.; Omar, H.; Huat, B.K.; Yousof, Z.B.M.; Ghiasi, V.: Deterministic rainfall induced landslide approaches, advantage and limitation. Electron. J. Geotech. Eng. 16, 1619–1650 (2011)

4. Kjekstad, O.; Highland, L.: Economic and social impacts of landslides. In: Zhou, L.; Ooi, B.; Meng, X. (Eds.) Landslides: Disaster Risk Reduction, pp. 573–587. Springer, Berlin (2009)

5. Ildir, B.: Turkiyede heyelanlarin dagilimi ve afetler yasasi ile ilgili uygulamalar (Landslide distribution in Turkey and applications based on disasters regulation). In: Onalp, A. (ed.) Proceedings of 2nd National Landslide Symposium Turkey, Sakarya University. pp. 1–9 (1995)

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