Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach

By M. Ercanoglu, Candan Gokceoglu. 2002

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Citations in the corpus

Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data (2005, methodology): “As a new approach to landslide hazard evaluation using GIS, data mining using fuzzy logic, and artificial neural network methods have been applied (Ercanoglu and Gokceoglu 2002; Pistocchi et al. 2002, Lee et al. 2003a, b, 2004b).”
Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran (2013, background): “The influence of plan curvature on the slope erosion processes is the convergence or divergence of water during downhill flow (Ercanoglu and Gokceoglu 2002; Oh and Pradhan 2011).”
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, background): “Water convergence and divergence during the ﬂ ow, which affect slope erosion processes, can be determined using plan curvature (Ercanoglu and Gokceoglu 2002; Oh and Pradhan 2011).”
Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling (2010, methodology): “Among the recent approaches to landslide susceptibility evaluation, some studies adopted fuzzy logic and artificial neural network models (Ercanoglu and Gokceoglu, 2002; Neaupane and Achet, 2004; Arora et al., 2004; Lee, 2007; Lee et al., 2003a,b, 2004; Gomez and Kavzoglu, 2005; Pradhan and Lee,…”
Use of geospatial data and fuzzy algebraic operators to landslide-hazard mapping (2009, methodology): “As a new approach to landslide-hazard evaluation using GIS, data mining, safety factor, fuzzy logic and artificial neural network models have been applied (Gomez and Kavzoglu, 2005; Xie et al. 2004; Ercanoglu and Gokceoglu 2002; Pistocchi et al. 2002; Lee et al. 2003a, b, 2004a; Tangestani 2004; Lee”
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): “Recently, other new methods have been applied for landslide susceptibility evaluation using fuzzy logic (Akgun et al. 2012b; Champatiray et al. 2007; Ercanoglu and Gokceoglu 2002; Muthu et al. 2008; Pourghasemi et al. 2012; Pradhan and Lee 2009; Saboya et al. 2006) and artificial neural network mode”
Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey) (2004, methodology): “, are needed to evaluate the landslide susceptibility, and they have been successfully applied in the recent years for different scales (for example, Lee et al., 2001, 2003, 2004; Ercanoglu and Gokceoglu, 2002; Tangestani, 2003).”