A quick method of early landslide identification based on dynamic susceptibility analysis using M-SVM method: a case study
Author:
Publisher
Springer Science and Business Media LLC
Subject
Geology,Geotechnical Engineering and Engineering Geology
Link
https://link.springer.com/content/pdf/10.1007/s10064-023-03440-9.pdf
Reference83 articles.
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2. Ahangari Nanehkaran Y, Pusatli T, Chengyong J, Chen J, Cemiloglu A, Azarafza M, Derakhshani R (2022) Application of machine learning techniques for the estimation of the safety factor in slope stability analysis. Water 14(22):3743. https://doi.org/10.3390/w14223743
3. Al-Najjar HA, 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. https://doi.org/10.1016/j.gsf.2020.09.002
4. Al-Najjar HAH, Pradhan B, Kalantar B, Sameen MI, Santosh M, Alamri A (2021) Landslide susceptibility modeling: an integrated novel method based on machine learning feature transformation. Remote Sens 13(16). https://doi.org/10.3390/rs13163281
5. Azarafza M, Azarafza M, Akgün H, Atkinson PM, Derakhshani R (2021) Deep learning-based landslide susceptibility mapping. Sci Rep 11(1):24112. https://doi.org/10.1038/s41598-021-03585-1
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