Landslide susceptibility modeling based on GIS and ensemble techniques
Author:
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/s12517-022-09974-8.pdf
Reference128 articles.
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2. Agterberg FP (1992) Combining indicator patterns in weights of evidence modeling for resource evaluation. Nonrenewable Resources 1:39–50. https://doi.org/10.1007/BF01782111
3. Ahmed B (2015) Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh. Landslides 12:1077–1095. https://doi.org/10.1007/s10346-014-0521-x
4. Aktas H, San BT (2019) Landslide susceptibility mapping using an automatic sampling algorithm based on two level random sampling. Comput Geosci 133:104329. https://doi.org/10.1016/j.cageo.2019.104329
5. 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(2):625–637. https://doi.org/10.1016/j.gsf.2020.09.002
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