Assessing gully erosion susceptibility using topographic derived attributes, multi-criteria decision-making, and machine learning classifiers-Reference-Cited by-同舟云学术

Assessing gully erosion susceptibility using topographic derived attributes, multi-criteria decision-making, and machine learning classifiers

Published:2021-01-01 Issue:1 Volume:12 Page:3035-3062
ISSN:1947-5705
Container-title:Geomatics, Natural Hazards and Risk
language:en
Short-container-title:Geomatics, Natural Hazards and Risk

Author:

Al-Bawi Ahmed J.¹,Al-Abadi Alaa M.¹,Pradhan Biswajeet²³⁴^ORCID,Alamri Abdullah M.⁵

Affiliation:

1. Department of Geology, College of Science, University of Basrah, Basrah, Iraq

2. The Centre for Advanced Modelling and Geospatial Information Systems, Faculty of Engineering and IT, University of Technology Sydney, NSW, Australia

3. Department of Energy and Mineral Resources Engineering, Sejong University, Gwangjin-gu, Seoul, Korea

4. Earth Observation Center, Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia

5. Department of Geology and Geophysics, College of Science, King Saud University, Riyadh, Saudi Arabia

Funder

University of Technology Sydney

King Saud University

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences,General Environmental Science

Link

https://www.tandfonline.com/doi/pdf/10.1080/19475705.2021.1994024

Reference44 articles.

1. Susceptibility mapping of gully erosion using GIS-based statistical bivariate models: a case study from Ali Al-Gharbi District, Maysan Governorate, southern Iraq

2. Water erosion susceptibility mapping by applying Stochastic Gradient Treeboost to the Imera Meridionale River Basin (Sicily, Italy)

3. Gully erosion susceptibility mapping using GIS-based multi-criteria decision analysis techniques

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