Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): a comparative assessment of

By D. Tien Bui, Biswajeet Pradhan, O. Lofman, Inge Revhaug, Ø. Dick. 2012

365 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Management, Monitoring, Policy and Law, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/6cd3577416aa3f335f797baad278f90c16f2dd05

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Citations in the corpus

Landslide susceptibility mapping using ensemble bivariate and multivariate statistical models in Fayfa area, Saudi Arabia (2015, methodology): “Tien Bui et al. (2012) and Pourghasemi et al. (2012a) indicated that the success rate method uses the training landslide pixels that have already been used for building the landslide models; thus, this method is not suitable for assessing the prediction capability of models.”
Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization (2018, methodology): “Success-rate curves have been constructed using groundwater potential maps and the number of spring lo- cations used in the training dataset (Tien Bui et al., 2012).”
Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran (2013, methodology): “…Gomez and Kavzoglu 2005; Nefeslioglu et al. 2008; Pradhan et al. 2010b; Pradhan and Lee 2009, 2010a, b; Yilmaz 2009a, b, 2010a, b; Zare et al. 2012; Bui et al. 2012b), neuro-fuzzy (Pradhan et al. 2010a; Vahidnia et al. 2010; Sezer et al. 2011; Oh and Pradhan 2011; Bui et al. 2012a), decision tree…”
A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India) (2016, methodology): “In addition, Tien Bui et al. (2012a) reported that results derived from SVM can produce better landslide susceptibility map compared to decision tree, and naïve bayes methods.”
Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods (2017, background): “…2012; Pradhan 2011; Saboya Jr et al. 2006; Tien Bui et al. 2012d), neuro-fuzzy (Oh and Pradhan 2011; Pradhan et al. 2010b; Sezer et al. 2013; Tien Bui et al. 2012c; Vahidnia et al. 2010), support vector machine (Hong et al. 2015a; Marjanovic et al. 2011; Peng et al. 2014; Pourghasemi et al.…”