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.

1. Schuster, R.L.: Socioeconomic significance of landslides. Spec. Rep. Natl. Res. Counc. Transp. Res. Board. 247, 12–35 (1996)

2. Dilley, M.; Chen, R.S.; Deichmann, U.; Lerner-Lam, A.; Arnold, M.; Agwe, J.; Buys, P.; Kjekstad, O.; Lyon, B.; Yetman, G.: Natural Disaster Hotspots: A Global Risk Analysis. World Bank Publications, Washington, DC (2005)

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)

Cited by 131 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Landslide dynamic susceptibility mapping in urban expansion area considering spatiotemporal land use and land cover change;Science of The Total Environment;2024-11

2. Dust source susceptibility in the lower Mesopotamian floodplain of Iraq;Remote Sensing Applications: Society and Environment;2024-11

3. Improving soil moisture prediction with deep learning and machine learning models;Computers and Electronics in Agriculture;2024-11

4. Distribution coefficient prediction using multimodal machine learning based on soil adsorption factors, XRF, and XRD spectrum data;Journal of Hazardous Materials;2024-10

5. Exploring the decision-making process of ensemble learning algorithms in landslide susceptibility mapping: Insights from local and global explainable AI analyses;Advances in Space Research;2024-10