Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances

By Hamid Reza Pourghasemi, Hamid Reza Moradi, S. M. F. Aghda. Natural Hazards, 2013

407 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Management, Monitoring, Policy and Law, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/133223799ef2eb5cd8900022c624900aa88cf018

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In Global and Planetary Change

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Citations in the corpus

Comparison of Support Vector Machine, Bayesian Logistic Regression, and Alternating Decision Tree Algorithms for Shallow Landslide Susceptibility Mapping along a Mountainous Road in the West of Iran (2020, background): “Expert knowledge-based models are typically based on questionnaires and expert opinions that may differ from one expert to another and also may suffer from cognitive limitations centered around uncertainty and subjectivity [36].”
GIS-based landslide susceptibility mapping using analytical hierarchy process (AHP) and certainty factor (CF) models for the Baozhong region of Baoji City, China (2015, methodology): “A value close to 0 illustrates that it is difficult to give information about the certainty of landslide occurrences (Pourghasemi et al. 2013c).”
Geostatistical Modeling to Capture Seismic‐Shaking Patterns From Earthquake‐Induced Landslides (2018, methodology): “We chose the ROC curve because it has a long history in landslide susceptibility modeling (e.g., Paolo et al., 2010; Pourghasemi et al., 2013), and because it is among the most common metrics for binary data (e.g., Hong et al., 2018; Tziritis & Lombardo, 2017).”
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): “…et al. 2008; Yilmaz 2009; Pradhan 2010; Bednarik and Pauditš 2010; Bai et al. 2010; Akgun et al. 2012a; Bui et al. 2011; Wang et al. 2013; Pourghasemi et al. 2013; Bhandary et al. 2013; Xu et al. 2013; Regmi et al. 2014), analytical hierarchy process, and frequency ratio (FR) (Akgun et…”
Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization (2018, methodology): “In this study, a ratio of 70/30 was selected randomly for generating the training and testing datasets (Pourghasemi et al., 2012, 2013a, b; Xu et al., 2012).”
A GIS-based comparative study of frequency ratio, statistical index and weights-of-evidence models in landslide susceptibility mapping (2016, methodology): “…2014a, b), spatial multicriteria decision analysis (MCDA) (Akgun and Turk 2010; Akgun 2012), weights of evidence (WoE) (Ozdemir and Altural 2013; Pourghasemi et al. 2013b, c; Regmi et al. 2014), statistical index (SI) (Bui et al. 2011; Regmi et al. 2014), index of entropy (IoE) model (Mihaela…”
Application of analytical hierarchy process, frequency ratio, and certainty factor models for groundwater potential mapping using GIS (2015, methodology): “Jha et al. (2010), Pourghasemi et al. (2013a), and Kaliraj et al. (2014) stated that GIS-based AHP model as an expert knowledge-based approach is very useful for solving complex problems.”
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, methodology): “…et al. 2014; Umar et al. 2014; Hong et al. 2016b), maximum entropy (Constantin et al. 2011; Pourghasemi et al. 2012b) and support vector machine (SVM) (Marjanovi (cid:1) c et al. 2011; Ballabio and Sterlacchini 2012; Pourghasemi et al. 2013a; Peng et al. 2014; Hong et al. 2016b) have been used.”
Assessment of Landslide-Prone Areas and Their Zonation Using Logistic Regression, LogitBoost, and NaïveBayes Machine-Learning Algorithms (2018, background): “It is emphasized that the aspect is a vital factor in instigating landslide susceptibility mapping [1,39].”