Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran

By Hamid Reza Pourghasemi, A. G. Jirandeh, Biswajeet Pradhan, Chong Xu, Candan Gokceoglu. Proceedings of the Indian Academy of Sciences, Earth and Planetary Sciences, 2013

340 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Management, Monitoring, Policy and Law

View Paper: https://www.semanticscholar.org/paper/9744c96c059d9517fc109f3ae8b90c8abe0ff472

How researchers see this paper

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

In Global and Planetary Change

In Management, Monitoring, Policy and Law

Citations in the corpus

Spatial Prediction of Rainfall-Induced Landslides Using Aggregating One-Dependence Estimators Classifier (2018, methodology): “However, there is no agreement on how to choose the best fit amount of landslide data for generating datasets such as Pradhan (2013) used the same landslide data amounts (50/ 50) for generating training and testing datasets whereas Saro Lee and Oh (2012) selected the landslide data amounts of 70/30…”
Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances (2013, methodology): “Recently, new landslide susceptibility assessment methods such as support vector machine (SVM) (Ballabio and Sterlacchini 2012; Marjanović et al. 2011; Yao et al. 2008; Yilmaz 2010; Xu et al. 2012b; Pourghasemi et al. 2013), decision tree methods (Nefeslioglu et al.”
Landslide susceptibility mapping based on GIS and support vector machine models for the Qianyang County, China (2016, methodology): “The topographic wetness index (TWI) describes the effect of topography on the location and size of saturated source areas of runoff generation, and was considered as another contributing factor (Pourghasemi et al. 2013b; Pradhan and Kim 2014).”
An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: a case study of Duzkoy district (2015, methodology): “In recent years, SVMs have been successfully applied to many problems including landslide susceptibility mapping (Pourghasemi et al. 2013; Pradhan 2013; Kavzoglu et al. 2014; Peng et al. 2014).”
A Support Vector Machine for Landslide Susceptibility Mapping in Gangwon Province, Korea (2017, background): “In summary, SVMs have a significant advantage when compared to other machine learning algorithms: the uniqueness of the solution [57].”
Spatial prediction of landslide susceptibility by combining evidential belief function, logistic regression and logistic model tree (2019, background): “TWI, which is relate to soil condition, is commonly applied to describe the effect of topography on the location (Pradhan 2013; Pourghasemi et al. 2012b, 2013).”
Groundwater qanat potential mapping using frequency ratio and Shannon’s entropy models in the Moghan watershed, Iran (2015, methodology): “Also profile curvature was calculated, and classified into 3 groups based on common standard classification (Pourghasemi et al. 2013b) including <(−0.001), (−0.001)–(0.001), and>(0.001) (Fig.”
A GIS-based comparative study of frequency ratio, statistical index and weights-of-evidence models in landslide susceptibility mapping (2016, methodology): “…2014a, b), spatial multicriteria decision analysis (MCDA) (Akgun and Turk 2010; Akgun 2012), weights of evidence (WoE) (Ozdemir and Altural 2013; Pourghasemi et al. 2013b, c; Regmi et al. 2014), statistical index (SI) (Bui et al. 2011; Regmi et al. 2014), index of entropy (IoE) model (Mihaela…”