Application of RBF and MLP Neural Networks Integrating with Rotation Forest in Modeling Landslide Susceptibility of Sampheling, Bhutan
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-16-7314-6_10
Reference66 articles.
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2. Achour Y, Pourghasemi HR (2019) How do machine learning techniques help in increasing accuracy of landslide susceptibility maps? Geosci Front. https://doi.org/10.1016/j.gsf.2019.10.001
3. Adriano B, Yokoya N, Miura H, Matsuoka M, Koshimura S (2020) A semiautomatic pixel-object method for detecting landslides using multitemporal ALOS-2 intensity images. Remote Sens 12(3):561. https://doi.org/10.3390/rs12030561
4. 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(2):165. https://doi.org/10.1007/s12517-015-2142-7
5. Akgun A, Sezer EA, Nefeslioglu HA, Gokceoglu C, Pradhan B (2012) An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Comput Geosci 38(1):23–34. https://doi.org/10.1016/j.cageo.2011.04.012
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