Landslide susceptibility analysis using probabilistic likelihood ratio model—a geospatial-based study

GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques

“…A greater ratio indicates a stronger relationship between a conditioning factor and flooding, and vice versa. If the FR value is higher than 1, the relationship is strong, and conversely weak if less than 1 (Lee and Talib 2005;Sujatha et al 2013). …”

“…It is regarded as a simple, easily understandable method (Yilmaz 2007). The greater the FR, the more substantial is the relationship between occurrence and specific variable (Pradhan 2010b;Sujatha et al 2013).…”

GIS based landslide susceptibility mapping of Tevankarai Ar sub-watershed, Kodaikkanal, India using binary logistic regression analysis

“…They require detailed geotechnical data and are limited to instances where ground conditions are fairly homogeneous (Dai and Lee 2002). Statistical methods are indirect mapping methods that compare the spatial distribution of landslides with the physical factors identified to cause landslides (Dai and Lee 2002;Süzen and Doyuran 2004;Ayalew and Yamagishi 2005;Gokceoglu et al 2005;Duman et al 2006;Lee and Pradhan 2007;Magliulo et al 2008;Bai et al 2010;Yilmaz 2010;Sujatha and Rajamanickam 2011b). They are used to obtain quantitative relationships between dependent and independent variable using linear or nonlinear models.…”

Landslide Susceptibility Evaluation Using Hybrid Integration of Evidential Belief Function and Machine Learning Techniques

“…The results showed that the RSCART model is the optimal model with the highest AUC values of 0.852 and 0.827, followed by LR and CART models. The results also illustrate that the hybrid model generally improves the prediction ability of a single landslide susceptibility model.Water 2020, 12, 113 2 of 29 weights of evidence [10][11][12], frequency ratio [13][14][15][16][17], logistic regression [18][19][20][21], linear multivariate regression, multivariate adaptive regression spline [22][23][24], and statistical index [25,26] have been widely used. However, these traditional statistical methods do not provide satisfactory evaluation of the correlation between landslide influencing factors [4,27].Therefore, machine learning technologies have drawn extensive attention, and many kinds of machine learning methods have been developed and used, such as classification and regression trees [28,29], adaptive neuro-fuzzy inference systems [30,31], fuzzy logic [32,33], alternating decision trees [34][35][36], support vector machine [37][38][39], artificial neural networks [40,41], and random forest [4,[42][43][44][45].…”

“…Water 2020, 12, 113 2 of 29 weights of evidence [10][11][12], frequency ratio [13][14][15][16][17], logistic regression [18][19][20][21], linear multivariate regression, multivariate adaptive regression spline [22][23][24], and statistical index [25,26] have been widely used. However, these traditional statistical methods do not provide satisfactory evaluation of the correlation between landslide influencing factors [4,27].…”