A comparative study of different machine learning methods coupled with GIS for landslide susceptibility assessment: a case study of N’fis basin, Marrakesh High Atlas (Morocco)-Reference-Cited by-同舟云学术

A comparative study of different machine learning methods coupled with GIS for landslide susceptibility assessment: a case study of N’fis basin, Marrakesh High Atlas (Morocco)

Published:2022-06 Issue:11 Volume:15 Page:
ISSN:1866-7511
Container-title:Arabian Journal of Geosciences
language:en
Short-container-title:Arab J Geosci

Author:

Ait Naceur Hassan,Igmoulan Brahim,Namous Mustapha,Amrhar Mostafa,Bourouay Omar,Ouayah Mustapha,Jadoud Mourad

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences,General Environmental Science

Link

https://link.springer.com/content/pdf/10.1007/s12517-022-10349-2.pdf

Reference63 articles.

1. Al-Najjar HH, Pradhan B (2021) Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks. Geosci Front 12(2):625–637

2. Althuwaynee OF, Pradhan B, Lee S (2016) A novel integrated model for assessing landslide susceptibility mapping using CHAID and AHP pair-wise comparison. Int J Remote Sens 37(5):1190–1209

3. Amaya A, Algouti A, Algouti A, El Aaggad N (2014) Mapping of mass movements hazard in the N’fis watershed, High Atlas, Morocco. Int J Innov Appl Stud 8(2):645

4. Bragagnolo L, da Silva RV, Grzybowski JMV (2020) Landslide susceptibility mapping with r landslide: a free open-source GIS-integrated tool based on artificial neural networks. Environ Modell Software 123:104565

5. Capparelli G, Tiranti D (2010) Application of the MoniFLaIR early warning system for rainfall-induced landslides in Piedmont region (Italy). Landslides 7(4):401–410

Cited by 5 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. To explore the optimal solution of different mapping units and classifiers and their application in the susceptibility evaluation of slope geological disasters;Ecological Indicators;2024-06

2. Implementation of random forest, adaptive boosting, and gradient boosting decision trees algorithms for gully erosion susceptibility mapping using remote sensing and GIS;Environmental Earth Sciences;2024-02

3. Landslide spatial prediction utilizing fuzzy unordered rules induction ensemble models: a case study in Thai Nguyen, Vietnam;Modeling Earth Systems and Environment;2023-12-02

4. Landslide Susceptibility Mapping Using Multi-Criteria Decision-Making (MCDM), Statistical, and Machine Learning Models in the Aube Department, France;Earth;2023-09-09

5. Head-cut gully erosion susceptibility mapping in semi-arid region using machine learning methods: insight from the high atlas, Morocco;Frontiers in Earth Science;2023-05-30