Landslide susceptibility prediction considering rock integrity and stress state: 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/s10064-023-03250-z.pdf
Reference40 articles.
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2. Akgun A, Erkan O (2016) Landslide susceptibility mapping by geographical information system-based multivariate statistical and deterministic models: in an artificial reservoir area at Northern Turkey. Arab J Geosci 9:165. https://doi.org/10.1007/s12517-015-2142-7
3. Anwer RM, Khan FS, van de Weijer J, Molinier M, Laaksonen J (2018) Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification. ISPRS J Photogramm Remote Sens 138:74–85. https://doi.org/10.1016/j.isprsjprs.2018.01.023
4. Azarafza M, Azarafza M, Akgün H, Atkinson PM, Derakhshani R (2021) Deep learning-based landslide susceptibility mapping. Sci Rep 11:24112. https://doi.org/10.1038/s41598-021-03585-1
5. Bandara S, Ohtsuka S (2017) Spatial distribution of landslides induced by the 2004 Mid-Niigata prefecture earthquake, Japan. Landslides 14:1877–1886. https://doi.org/10.1007/s10346-017-0819-6
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