Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey)

By M. Ercanoglu, Candan Gokceoglu. 2004

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An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm (2012, background): “…Lamelas et al., 2008; Gorum et al., 2008; Turer et al., 2008; Nefeslioglu et al., 2008a; Nefeslioglu and Gokceoglu, 2011), soft computing (e.g., Ercanoglu and Gokceoglu, 2002, 2004; Arora et al., 2004; Lee et al., 2004; Ercanoglu, 2005; Lui et al., 2006; Kanungo et al., 2006; Melchiorre et…”
Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran (2013, methodology): “…other new methods have been applied for landslide susceptibility evaluation using soft computing and data mining approaches such as fuzzy logic (Ercanoglu and Gokceoglu 2004; Muthu et al. 2008; Pradhan and Buchroithner 2010; Pradhan and Pirasteh 2010; Pradhan 2010a, b, 2011a, b; Pradhan et al.…”
GIS-based landslide susceptibility mapping using analytical hierarchy process (AHP) and certainty factor (CF) models for the Baozhong region of Baoji City, China (2015, background): “The first fundamental step in landslide susceptibility assessments is to acquire information regarding landslides that have occurred in the past to support future spatial prediction (Ercanoglu and Gokceoglu 2004).”
Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances (2013, background): “Besides, landslide inventory mapping is the most fundamental step in any landslide susceptibility and hazard modeling (Ercanoglu and Gokceoglu 2004).”
Landslide susceptibility mapping using ensemble bivariate and multivariate statistical models in Fayfa area, Saudi Arabia (2015, background): “The slope aspect represents the direction of maximum slope of the terrain surface (Ercanoglu and Gokceoglu 2004).”
Application of statistical index and index of entropy methods to landslide susceptibility assessment in Gongliu (Xinjiang, China) (2016, background): “The altitude is one of the frequently used significant landslides conditioning parameter (Youssef et al. 2015; Ercanoglu et al. 2004; Rozos et al. 2011).”
Application of a fuzzy operator to susceptibility estimations of coal mine subsidence in Taebaek City, Korea (2010, background): “If the RMSE is close to 0, it indicates a good relation between the observed ground subsidence and estimated susceptibility ( Ercanoglu and Gokceoglu 2004 ).”
Assessment of Landslide-Prone Areas and Their Zonation Using Logistic Regression, LogitBoost, and NaïveBayes Machine-Learning Algorithms (2018, background): “Generally, landslides have more tendencies to occur at high altitude [45].”
Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS (2012, background): “2008; Pradhan and Lee, 2010a, c; Pradhan et al., 2010a), fuzzy logic (Akgun et al., 2011; Ercanoglu and Gokceoglu, 2002, 2004; Kanungo et al., 2008; Pradhan, 2010a), decision tree (Nefeslioglu et al., 2010), and neuro-fuzzy (Kanungo et al., 2006; Oh and Pradhan, 2011; Pradhan et al., 2010b; Sezer…”
Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat - Turkey) (2009, methodology): “…new techniques such as; fuzzy-logic, artificial neural networks (ANNs), neuro-fuzzy model, etc. (Pistocchi et al., 2002; Lee et al., 2003a, b, 2004; Ercanoglu and Gokceoglu, 2004; Gomez and Kavzoglu, 2005; Yesilnacar and Topal, 2005; etc.) were used to evaluate the landslide susceptibility,…”