Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree

This study introduces a framework for training and validation of shallow landslide susceptibility models by using the latest statistical methods and demonstrates the benefit of selecting the optimal machine learning techniques with proper conditioning selection method in shallow landslide susceptible mapping.