Application and Comparison of Decision Tree-Based Machine Learning Methods in Landside Susceptibility Assessment at Pauri Garhwal Area, Uttarakhand, India

99 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Spatial prediction of landslide susceptibility by combining evidential belief function, logistic regression and logistic model tree (2019, methodology): “…proposed for landslide modeling, such as J48 decision trees (Hong et al. 2018; Tien Bui et al. 2014), alternating decision trees (Chen et al. 2017; Pham et al. 2017b), random forests (Tsangaratos et al. 2017; Chen et al. 2018f), classification and regression tree (Youssef et al. 2016; Pham et al.…”
Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization (2018, methodology): “The first part is used for model building, which is called a training dataset, and the other part is utilized for validation of the model performance, called a testing dataset (Pham et al., 2017a).”
Landslide susceptibility modelling using different advanced decision trees methods (2018, background): “…2014; Chen et al. 2017a), support vector machines (Kavzoglu, Sahin, and Colkesen 2014; Pourghasemi et al. 2013; Yilmaz 2010; Dou et al. 2015; Pham et al. 2017a), decision trees (Alkhasawneh et al. 2014; Nefeslioglu et al. 2010), and neuro-fuzzy (Vahidnia et al. 2010) are widely used…”