Evaluating scale effects of topographic variables in landslide susceptibility models using GIS-based machine learning techniques
Author:
Publisher
Springer Science and Business Media LLC
Subject
Multidisciplinary
Link
http://www.nature.com/articles/s41598-019-48773-2.pdf
Reference64 articles.
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3. Merghadi, A., Abderrahmane, B. & Tien Bui, D. Landslide Susceptibility Assessment at Mila Basin (Algeria): A Comparative Assessment of Prediction Capability of Advanced Machine Learning Methods. ISPRS Int. J. Geo-Information 7 (2018).
4. Chen, W. et al. Spatial prediction of groundwater potentiality using ANFIS ensembled with teaching-learning-based and biogeography-based optimization. J. Hydrol, https://doi.org/10.1016/j.jhydrol.2019.03.013 (2019).
5. Chen, W. et al. Novel Hybrid Integration Approach of Bagging-Based Fisher’s Linear Discriminant Function for Groundwater Potential Analysis. Nat. Resour. Res, https://doi.org/10.1007/s11053-019-09465-w (2019).
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