An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm

By Aykut Akgün, Ebru Akcapinar Sezer, Hakan Ahmet Nefeslioglu, Candan Gokceoglu, Biswajeet Pradhan. Computational Geosciences, 2012

344 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/f939e024911afa7e352a06f18b5cd5036299d1f6

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In Civil and Structural Engineering

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Citations in the corpus

Landslide susceptibility evaluation and hazard zonation techniques – a review (2020, methodology): “This method utilizes the correlation between each of the responsible causative factor classes and the spatial distribution of past landslides in the area (Chimidi et al. 2017; Girma et al. 2015; Moung-Jin et al. 2014; Akgun et al. 2012; Pradhan and Lee 2009; Lee 2005).”
Landslide susceptibility mapping based on GIS and support vector machine models for the Qianyang County, China (2016, methodology): “In the study, the receiver operating characteristics (ROC) curve was used to assess the overall performance of the four used models, because the ROC curve is helpful for representing the quality of deterministic and probabilistic forecast systems (Akgun et al. 2012; Youssef et al. 2015a, b).”
Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS (2012, background): “, 2010a), fuzzy logic (Akgun et al., 2011; Ercanoglu and Gokceoglu, 2002, 2004; Kanungo et al., 2008; Pradhan, 2010a), decision tree (Nefeslioglu et al.”
Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods (2017, methodology): “Over the last decades, many studies in landslide susceptibility mapping using GIS have been carried out in many regions over the world such as weights of evidence (Kayastha et al. 2012; Mohammady et al. 2012; Ozdemir and Altural 2013; Pourghasemi et al. 2013b; Regmi et al. 2014), frequency ratio (Ch”
GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran (2015, methodology): “…the accuracy of the three spring potential maps created in the current research using CART, RF, and BRT models, the receiver operating characteristics (ROC) curve was used (Ozdemir and Altural 2013; Akgun et al. 2012; Mohammady et al. 2012; Pourghasemi et al. 2012a; Naghibi and Pourghasemi 2015).”
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…”
Landslide susceptibility mapping using ensemble bivariate and multivariate statistical models in Fayfa area, Saudi Arabia (2015, methodology): “2014), neuro-fuzzy-based study (Akgun et al. 2012; Oh and Pradhan 2011), fuzzy logic (Pradhan 2011), and artificial neural network (ANN) (Wu et al.”
A GIS-based comparative study of frequency ratio, statistical index and weights-of-evidence models in landslide susceptibility mapping (2016, background): “…2011; Devkota et al. 2013), artificial neural network (ANN) (Nefeslioglu et al. 2008; Poudyal et al. 2010; Yilmaz 2009a, b, 2010a, b), fuzzy logic (Akgun et al. 2012b; Pourghasemi et al. 2012; Sharma et al. 2013; Guettouche 2013), support vector machine (SVM) (Yilmaz 2010b; Marjanović et al.…”
A GIS-based extended fuzzy multi-criteria evaluation for landslide susceptibility mapping (2014, methodology): “GIS-based MCDA can be used together with fuzzy set theory to model imprecise objectives in a variety of research areas (Chang et al., 2008; Yonca Aydin et al., 2013), especially for landslide susceptibility mapping purposes (Akgun et al., 2012; Shadman et al., 2013).”