Landslide susceptibility assessment using SVM machine learning algorithm

By Milos Marjanovic, Milos Kovacevic, Branislav Bajat, Vit Vozenilek. 2011

580 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/7b1ff07b4493bbc1c7d6a9b72328bde0d40164ec

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In Global and Planetary Change

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Citations in the corpus

A GIS-based comparative study of frequency ratio, statistical index and weights-of-evidence models in landslide susceptibility mapping (2016, methodology): “…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 et al. 2011; Devkota et al. 2013), artificial neural network (ANN) (Nefeslioglu et al. 2008; Poudyal et al. 2010; Yilmaz 2009a, b, 2010a, b),…”
Examining Hybrid and Single SVM Models with Different Kernels to Predict Rock Brittleness (2020, background): “Altindag [78] and Yarali and Soyer [79] developed the following formulas, respectively: B 3 = σ c * σ t /2.”
Landslide susceptibility mapping based on GIS and support vector machine models for the Qianyang County, China (2016, methodology): “Brenning (2005) obtained sufficiently smooth prediction surfaces for creating susceptibility map by using SVM. Yao et al. (2008) used the SVM in landslide susceptibility mapping, they found that SVM was a useful tool in landslide susceptibility assessment, and they found that the SVM had better pred”
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): “…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. 2013a; Tien Bui et al. 2012b; Xu et al. 2012).”
Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN) (2018, methodology): “…et al. 2014; Umar et al. 2014; Hong et al. 2016b), maximum entropy (Constantin et al. 2011; Pourghasemi et al. 2012b) and support vector machine (SVM) (Marjanovi (cid:1) c et al. 2011; Ballabio and Sterlacchini 2012; Pourghasemi et al. 2013a; Peng et al. 2014; Hong et al. 2016b) have been used.”