Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression

By Taskin Kavzoglu, E. Kutlug Sahin, Ismail Colkesen. Landslides, 2014

640 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/8a274bf7fa3405435387be43667432c44ce9ab24

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In Global and Planetary Change

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Citations in the corpus

A GIS-based comparative evaluation of analytical hierarchy process and frequency ratio models for landslide susceptibility mapping (2017, background): “In turn, these conditions have the potential to affect slope stability and generate slope failure (Kavzoglu et al., 2014; Meng et al., 2015).”
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): “Lithology can be regarded as one of the most vital conditioning factors in LSM because strength and the rocks and soils permeability are in ﬂ uenced directly by lithological characteristics (Kavzoglu et al. 2014).”
A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction (2019, background): “Some typical expert systems are fuzzy mathematical models (Kirschbaum et al. 2016), analytical hierarchy processes (Le et al. 2015; Nefeslioglu et al. 2013), multicriteria evaluation techniques (Ahmed 2015), etc.”
An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: a case study of Duzkoy district (2015, methodology): “…neural networks (Gomez and Kavzoglu 2005; Yilmaz 2009; Pradhan and Lee 2010), support vector machines (SVMs) (Yao et al. 2008; Yilmaz 2010; Kavzoglu et al. 2014), decision trees (DTs) (Hwang et al. 2009; Abdallah 2010; Bui et al. 2012) and logistic regression (LR) (Ayalew and…”
Comparison of Support Vector Machine, Bayesian Logistic Regression, and Alternating Decision Tree Algorithms for Shallow Landslide Susceptibility Mapping along a Mountainous Road in the West of Iran (2020, methodology): “SVM, in particular, can handle complex and non-linear datasets [35], and thus, is a robust benchmark model”