Spatial modeling and susceptibility zonation of landslides using random forest, naïve bayes and K-nearest neighbor in a complicated terrain-Reference-Cited by-同舟云学术

Spatial modeling and susceptibility zonation of landslides using random forest, naïve bayes and K-nearest neighbor in a complicated terrain

Published:2021-06-24 Issue:3 Volume:14 Page:1227-1243
ISSN:1865-0473
Container-title:Earth Science Informatics
language:en
Short-container-title:Earth Sci Inform

Author:

Abu El-Magd Sherif Ahmed,Ali Sk Ajim,Pham Quoc Bao

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences

Link

https://link.springer.com/content/pdf/10.1007/s12145-021-00653-y.pdf

Reference74 articles.

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2. Abu El-Magd SA, Amer RA, Embaby A (2020) Multi-criteria decision-making for the analysis of flash floods: A case study of Awlad Toq-Sherq, Southeast Sohag, Egypt. J African Earth Sci 162:103709

3. Abu El-Magd SA, Pradhan B, Alamri A (2021) Machine learning algorithm for flash flood prediction mapping in Wadi El-Laqeita and surroundings, Central Eastern Desert. Egypt Arabian Journal of Geosciences 14:323. https://doi.org/10.1007/s12517-021-06466-z

4. Achour Y, Pourghasemi HR (2020) How do machine learning techniques help in increasing accuracy of landslide susceptibility maps? Geosci Front 11:871–883. https://doi.org/10.1016/j.gsf.2019.10.001

5. Aditian A, Kubota T, Shinohara Y (2018) Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of ambon, Indonesia. Geomorphology 318:101–111

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