Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat - Turkey)

By Isik Yilmaz. Computational Geosciences, 2009

906 citations recorded by the source corpus.

Subfields: Management, Monitoring, Policy and Law, Mechanical Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/424c32ae1d3cbef94286d72ca44581a857005a80

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Citations in the corpus

A comparative study for landslide susceptibility mapping using GIS-based multi-criteria decision analysis (MCDA), logistic regression (LR) and association rule mining (ARM) (2016, methodology): “LR is applied in many studies to assess landslide susceptibility (e.g. Dai and Lee, 2001; Ohlmacher and Davis, 2003; Guzzetti et al., 2005; Ayalew and Yamagishi, 2005; Yilmaz, 2009; Pradhan and Lee, 2010b; Bai et al., 2011; Ozdemir and Altural, 2013; Kavzoglu et al., 2014).”
Ensemble-based landslide susceptibility maps in Jinbu area, Korea (2012, methodology): “…Monte Carlo methods also have been applied (Lee et al. 2004, 2006, 2007; Lee and Evangelista 2006; Shou et al. 2009; Tangestani 2009; Yalcin 2008; Yilmaz 2009, 2010a, b; Park 2011; Pradhan et al. 2010; Pradhan and Lee 2010b, 2010c; Pradhan 2011a, b; Suh et al. 2011; Wang et al. 2011; Akgun and…”
Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio, logistic regression, and fuzzy logic methods at the central Zab basin, Iran (2015, methodology): “…statistical analysis is the most frequent method in publications including logistic regression (LR) (Gorsevski et al. 2006; Nefeslioglu et al. 2008; Yilmaz 2009; Pradhan 2010; Bednarik and Pauditš 2010; Bai et al. 2010; Akgun et al. 2012a; Bui et al. 2011; Wang et al. 2013; Pourghasemi et al.…”
Landslide susceptibility mapping using ensemble bivariate and multivariate statistical models in Fayfa area, Saudi Arabia (2015, methodology): “The LR model can perform multivariate statistical analysis; furthermore, it analyzes the influence of classes of each conditioning factor individually on the landslides (Yilmaz 2009).”
Space-time landslide predictive modelling (2019, background): “TWI is a proxy for the ability of a given area to retain surface water as a function of the terrain gradient and the upslope contributing area, favouring infiltration and the increase of the pore water pressure at depth, and, hence, slope instability (e.g., Yilmaz, 2009; Cama et al., 2017).”
Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran (2013, methodology): “…neural network models (Lee et al. 2004; 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…”
An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm (2012, background): “: þ903122977735; fax ail address: cgokce@hacettepe.edu.tr (C. Gokc e cite this article as: Akgun, A., et al. a Mamdani fuzzy algorithm.”
Spatial prediction of landslide susceptibility by combining evidential belief function, logistic regression and logistic model tree (2019, methodology): “STI characterizes the process of erosion and deposition (Yilmaz 2009; Conforti et al. 2011).”
Application of statistical index and index of entropy methods to landslide susceptibility assessment in Gongliu (Xinjiang, China) (2016, background): “Many of the recent studies have applied probabilistic models (Lee and Dan 2005; Lee and Sambath 2006; Yilmaz 2009; Akgun et al. 2008).”