A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas

By Debi Prasanna Kanungo, M. Arora, S. Sarkar, R. Gupta. 2006

597 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Management, Monitoring, Policy and Law, Safety, Risk, Reliability and Quality

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Citations in the corpus

Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China. (2018, unknown): “, 2015), ANN-fuzzy logic technique (Kanungo et al., 2006), ANN-MaxEnt-SVM (Chen et al.”
Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS (2012, methodology): “Frequency ratio analysis was carried out on the classification results and landslide location data (Kanungo et al., 2006).”
Landslide susceptibility assessment using SVM machine learning algorithm (2011, background): “It has been proven successful in numerous case studies (Falaschi et al., 2009; Bai et al., 2010; Kanungo et al., 2006), but lately it has been broadly challenged by other machine learning approaches.”
Artificial Neural Network (ANN) and Regression Tree (CART) applications for the indirect estimation of unsaturated soil shear strength parameters (2014, methodology): “ANN models (Arora et al., 2004; Gómez and Kavzoglu, 2005; Yesilnacar and Topal, 2005; Kanungo et al., 2006; Nefeslioglu et al., 2008) have been implemented for LSZ studies.”
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): “The MLP can separate non-linear data (Lee et al. 2003), exploit helpful information from imprecise data, and produce acceptable results even when the inputs are not perfect (Ermini et al. 2005; Kanungo et al. 2006).”
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): “…(MCE), and multi-criteria decision analysis (MCDA) have been successfully used by different authors in landslide susceptibility mapping (e.g. Kanungo et al., 2006; Ayenew and Barbieri, 2005; Abella and van Westen, 2007; Akgun and Turk,. 2010; Pourghasemi et al., 2012; Ozdemir and Altural,.…”
Prediction of shear strength parameters of soils using artificial neural networks and multivariate regression methods (2012, background): “The number of hidden layers and their neurons are often determined by trial and error (Kanungo et al., 2006).”
Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling (2010, methodology): “Relative frequency ratio analysis was performed on the classification results and landslide location test data (Kanungo et al., 2006).”
An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm (2012, background): “…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 al., 2008; Nefeslioglu et al., 2008b, 2010; Pradhan et al., 2009, 2010; Caniani et al., 2008; Yilmaz, 2009;…”
Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues (2013, background): “, 2005, Yilmaz, 2009a), linear regression (Atkinson and Massari, 1998), fuzzy membership (Kanungo et al., 2006), conditional probability or Bayesian methods (Yilmaz, 2010a).”