Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling

By Biswajeet Pradhan, Saro Lee. Environmental Modelling & Software, 2010

918 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/bd185e4f80d1fa2def96815269ec60dff862a7a1

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In Management, Monitoring, Policy and Law

In Mechanical Engineering

In Safety, Risk, Reliability and Quality

Citations in the corpus

Ensemble-based landslide susceptibility maps in Jinbu area, Korea (2012, background): “…landslide susceptibility mapping (Lee 2005, 2007; Dong et al. 2009; Bai et al. 2011; Nandi and Shakoor 2010; Oh and Lee 2010; Pradhan et al. 2008; Pradhan and Lee 2010a; Bai et al. 2011; Lepore et al. 2011; Intarawichian and Dasananda 2011; Erener and Düzgün 2011; Ercanoglu and Temiz 2011;…”
Landslide susceptibility evaluation and hazard zonation techniques – a review (2020, methodology): “set of data or information relating to representative mapping (Pradhan and Lee 2010; Nefeslioglu et al., 2008; Garrett 1994).”
Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances (2013, background): “In an ideal landslide susceptibility map, the frequency ratio value is increasing from a low to a very high susceptibility zones (Pradhan and Lee 2010a, b; Pourghasemi et al. 2012c).”
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): “LR coefficients can be used to estimate ratios for each of the independent variables in the model (Pradhan and Lee 2010a).”
A GIS-based comparative evaluation of analytical hierarchy process and frequency ratio models for landslide susceptibility mapping (2017, background): “The presence of dense green vegetation implies high NDVI values (Pradhan & Lee, 2010; Shahabi et al., 2015).”
An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: a case study of Duzkoy district (2015, methodology): “…machines (SVMs) (Yao et al. 2008; Yilmaz 2010; Kavzoglu et al. 2014), decision trees (DTs) (Hwang et al. 2009; Abdallah 2010; Bui et al. 2012) and logistic regression (LR) (Ayalew and Yamagishi 2005; Yao et al. 2008; Pradhan and Lee 2010) have been utilized to produce landslide susceptibility maps.”
A GIS-based comparative study of frequency ratio, statistical index and weights-of-evidence models in landslide susceptibility mapping (2016, methodology): “…Xu et al. 2012b; Devkota et al. 2013; Ozdemir and Altural 2013; Kundu et al. 2013; Park et al. 2013; Grozavu et al. 2013) and bivariate models (Pradhan and Youssef 2010; Pareek et al. 2010; Pradhan and Lee 2010; Pourghasemi et al. 2013a), has also been applied to landslide susceptibility…”
Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China (2018, methodology): “7 The average merit of each causal factor in (a) colluvia landslide (b) rockfall 306 Although all the selected factors are relevant to landslides, but it is proved that the less important factors may 307 cause noise and reduce the prediction accuracy (Pradhan and Lee, 2010b; Pham et al., 2016a).”
A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India) (2016, background): “In the case of landslide prediction, the main purpose of LR is to find the best fitting algorithm to analyze the spatial relationship between the presence or absence of a landslide event and a set of affecting factors (Pradhan and Lee, 2010a).”
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…”