Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models

By Biswajeet Pradhan, Saro Lee. 2010

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Citations in the corpus

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).”
Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks (2016, background): “The context size m is the surrounding area around the pixel to be classified and is chosen among m = [5, 15, 25, 35, 45]; the pooling dimension is chosen among p = [2, 4, 6, 8, 10]; the number of filters is chosen among k = [50, 100, 150, 200]; and the filter size n is chosen, as all of the allowed ”
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 novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment (2018, background): “Hence, the first significant step in landside susceptibility assessment is to perform the spatial prediction of landslides at a regional scale (Pradhan and Lee 2010).”
Landslide susceptibility mapping using ensemble bivariate and multivariate statistical models in Fayfa area, Saudi Arabia (2015, background): “Recently, researchers have exerted considerable efforts to produce landslide susceptibility maps using geographic information system (GIS) (Lee and Pradhan 2007; Pourghasemi et al. 2013; Pradhan and Lee 2010b; Armas 2014).”
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): “LR is applied in many studies to assess landslide susceptibility (e.g. Dai and Lee, 2001; Ohlmacher and Davis, 2003; Guzzetti et al., 2005; Ayalew and Yamagishi, 2005; Yilmaz, 2009; Pradhan and Lee, 2010b; Bai et al., 2011; Ozdemir and Altural, 2013; Kavzoglu et al., 2014).”
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;…”
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).”
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): “In different studies, frequency ratio (Lee and Pradhan 2007; Pradhan and Lee 2010a; Nourani et al. 2014; Shahabi et al. 2014; Umar et al. 2014; Hong et al. 2016b), weights of evidence (Pourghasemi et al. 2012a, 2012b), ANN (Pradhan and Lee 2010b; Zare et al. 2013; Conforti et al. 2014; Nourani et…”
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…”
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, background): “Pradhan and Lee (2010) also reported that it can be finalized based on percentage of area of the study region.”