Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela

By H. Gómez, Taskin Kavzoglu. 2005

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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, background): “Gomez and Kavzoglu (2005) refer to Ochoa (1978), arguing that soil texture varies with altitude, as the grain size increases with the altitude.”
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): “…TWI is described by the ﬂ ow accumulation theoretical measure, where soil moisture affects the slope material, which results in pore water pressure and diminishes soil stability (G (cid:1) omez and Kavzoglu 2005; Yilmaz 2009), and could be regarded as an in ﬂ uential factor in landslide studies.”
Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression (2014, methodology): “It is an important factor in landslide activity since longer slope lengths increase the potential of erosive agents to transport materials downslope (Gómez and Kavzoglu 2005).”
Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio, logistic regression, and fuzzy logic methods at the central Zab basin, Iran (2015, background): “…and Lee 2009; Saboya et al. 2006) and artificial neural network models (Biswajeet and Saro 2007; Chauhan et al. 2010; Choi et al. 2012; Conforti et al. 2014; Ermini et al. 2005; Gomez and Kavzoglu 2005; Kawabata and Bandibas 2009; Nefeslioglu et al. 2008; Pradhan and Lee 2010b; Yilmaz 2009).”
Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran (2013, unknown): “…Pradhan 2010a, b, 2011a, b; Pradhan et al. 2009; Akgun et al. 2011b; Pourghasemi et al. 2012c), artificial 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;…”
Landslide susceptibility evaluation and hazard zonation techniques – a review (2020, methodology): “…ANN method is used which includes back propagation learning algorithm and is capable of developing rules for weight assignment for the respective factors (Kanungo et al. 2006; Yesilnacar and Topal 2005; Gómez and Kavzoglu 2005; Lee et al. 2004; Zhou et al. 2002; Haykin 1999; Ripley 1996).”
A comparative assessment of canonical correlation forest, random forest, rotation forest and logistic regression methods for landslide susceptibility mapping (2018, methodology): “functional trees and logistic model trees) (Gomez and Kavzoglu 2005; Kavzoglu, Sahin, and Colkesen 2014, 2015a; Tien Bui et al. 2016a,b; Colkesen, Sahin, and Kavzoglu 2016; Pham et al. 2016).”
Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat - Turkey) (2009, methodology): “Another problem is that the data requirements for deterministic models can be prohibitive, and frequently it is impossible to acquire the input data necessary to use the model effectively (Gomez and Kavzoglu, 2005).”
Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling (2010, methodology): “…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, 2007, 2009a, 2009b, 2009c; Pradhan et al., 2009; Youssef et al., 2009).”