Assessment of landslide susceptibility using machine learning classifiers in Ziz upper watershed, SE Morocco-Reference-Cited by-同舟云学术

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Assessment of landslide susceptibility using machine learning classifiers in Ziz upper watershed, SE Morocco

Published:2023-08-21 Issue: Volume: Page:1-28
ISSN:0272-3646
Container-title:Physical Geography
language:en
Short-container-title:Physical Geography

Author:

Manaouch Mohamed¹^ORCID,Sadiki Mohamed²,Aghad Mohamed¹,Bao Pham Quoc³,Batchi Mohcine¹,Al Karkouri Jamal¹

Affiliation:

1. Laboratory of Environment, Societies and Territories, Ibn Tofail University, Kenitra, Morocco

2. Laboratory of Geosciences, Ibn Tofail University, Kenitra, Morocco

3. Faculty of Natural Sciences, Institute of Earth Sciences, University of Silesia in Katowice, Sosnowiec, Poland

Funder

no funding

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences,Earth and Planetary Sciences (miscellaneous),Atmospheric Science,General Environmental Science

Link

https://www.tandfonline.com/doi/pdf/10.1080/02723646.2023.2250174

Reference60 articles.

1. Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia

2. A Comparative Study of Deep Learning Models for Diagnosing Glaucoma From Fundus Images

3. Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks

4. Selecting optimal conditioning parameters for landslide susceptibility: an experimental research on Aqabat Al-Sulbat, Saudi Arabia

5. A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility

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