Machine learning solution for regional landslide susceptibility based on fault zone division strategy
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11629-023-8202-7.pdf
Reference41 articles.
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2. Aditian A, Kubota T, Shinohara Y (2018) Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia. Geomorphology 318: 101–111. https://doi.org/10.1016/j.geomorph.2018.06.006
3. Al-Najjar HAH, Pradhan B (2021) Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks. Geosci Front 12: 625–637. https://doi.org/10.1016/j.gsf.2020.09.002
4. Breiman L (2001) Random forests. Mach Learn 45: 5–32. https://doi.org/10.1023/A:1010933404324
5. Chen W, Chen X, Peng JB, et al. (2021) Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer. Geosci Front 12: 93–107. https://doi.org/10.1016/j.gsf.2020.07.012
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