Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data

By Saro Lee. 2005

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Spatial Prediction of Rainfall-Induced Landslides Using Aggregating One-Dependence Estimators Classifier (2018, methodology): “The LR is one of the most popular methods for proper landslide prediction (Ayalew and Yamagishi 2005; Ohlmacher and Davis 2003a, b; Lee 2005).”
Comparison of GA-BP and PSO-BP neural network models with initial BP model for rainfall-induced landslides risk assessment in regional scale: a case study in Sichuan, China (2019, unknown): “2014a; Lee 2005; Pradhan and Lee 2010; Bui 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, methodology): “This conditioning factor is frequently used in preparing landslide susceptibility maps (Ercanoglu et al. 2004; Lee et al. 2004; Saha et al. 2005; Lee 2005; Thanh and De Smedt 2012; Pourghasemi et al. 2012a, b).”
An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm (2012, methodology): “…several methods are available for this, the area under the curve obtained from the ROC (receiver operating characteristics) plot is the most preferred and applicable type of statistical assessment for this purpose (Lee, 2005; Yilmaz, 2010a, b; Akgun and Turk, 2010; Nefeslioglu et al., 2010).”
Application of statistical index and index of entropy methods to landslide susceptibility assessment in Gongliu (Xinjiang, China) (2016, methodology): “The statistical models, such as the logistic regression, bivariate and multivariate models, have also been applied to landslide susceptibility mapping (Ayalew and Yamagishi 2005; Lee 2005a; Yesilnacar and Topal 2005; Lee and Sambath 2006; Van Den Eeckhaut et al. 2006; Pradhan 2010).”
Investigating landslide susceptibility procedures in Greece (2019, background): “For that reason, five different bivariate statistical models such as frequency ratio (FR) (Lee 2005; Lee and Sambath 2006; Lee and Pradhan 2007; Yilmaz 2009; Pradhan and Lee 2010; Oh et al. 2010; Choi et al. 2012; Sharma et al. 2014; Meten et al. 2015), landslide relative frequency (LRF)…”
Evolution and Prediction of Landscape Pattern and Habitat Quality Based on CA-Markov and InVEST Model in Hubei Section of Three Gorges Reservoir Area (TGRA) (2018, background): “The goal of LR is to find the best fitting (yet reasonable) model to describe the relationship between a dependent variable (the presence or absence of event) and a set of independent variables [58,59].”
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 by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances (2013, background): “The goal of logistic regression is to find the best model to describe the relationship between a dependent variable and multiple independent variables (Ohlmacher and Davis 2003; Lee 2005; Ozdemir 2011).”
Implementation of reconstructed geomorphologic units in landslide susceptibility mapping: the Melen Gorge (NW Turkey) (2008, methodology): “In literature, the logistic regression technique is widely used in producing landslide susceptibility maps (e.g., Atkinson and Massari 1998; Gorsevski et al. 2000; Lee and Min 2001; Ohlmacher and Davis 2003; Lee 2004, 2005; Ayalew and Yamagishi 2005; Can et al. 2005; Duman et al. 2006; Nefeslioglu”
Landslide detection from an open satellite imagery and digital elevation model dataset using attention boosted convolutional neural networks (2020, methodology): “For example, fuzzy logic (Ercanoglu and Gokceoglu 2002), logistic regression (Lee 2005), analytical hierarchy process (Kayastha et al. 2013), evidential belief function (Althuwaynee et al. 2012), weighted linear combination method (Ayalew et al. 2004), multivariate statistics analysis (Komac 2006),…”