An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps

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Prediction of shear strength parameters of soils using artificial neural networks and multivariate regression methods (2012, methodology): “Nefeslioglu et al. (2008) employed logistic regression and artificial neural networks for preparation of landslide susceptibility maps.”
Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances (2013, background): “In mountainous region, any disturbance on natural slopes, such as road cutting, may cause the initiation of mass movements (Nefeslioglu et al. 2008).”
A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction (2019, methodology): “2016), artificial neural networks (ANNs) (Gorsevski et al. 2016; Lee et al. 2007; Nefeslioglu et al. 2008), decision tree models (Althuwaynee et al.”
Spatial Prediction of Rainfall-Induced Landslides Using Aggregating One-Dependence Estimators Classifier (2018, background): “Road development creates instability of hill slopes thus influence the landslide occurrence (Nefeslioglu et al. 2008).”
A comparative study of landslide susceptibility maps using logistic regression, frequency ratio, decision tree, weights of evidence and artificial neural network (2016, background): “There are no exact mathematical rules to determine the required minimum size these subsets (Nefeslioglu et al., 2008).”