Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia

Landslide Susceptibility Modeling Based on GIS and Novel Bagging-Based Kernel Logistic Regression

“…Statistical models are most commonly used in landslide susceptibility mapping, which are based on the analysis of the relationships between influencing factors and existing landslides [7]. In these statistical approaches, bivariate and multivariate statistical techniques are used for landslide susceptibility mapping throughout the world, including frequency ratio [8][9][10], index of entropy [11][12][13][14][15], bivariate statistical analysis [16], multivariate adaptive regression spline [17], analytical hierarchy process [18,19], statistical index [20,21], weight of evidence [13,21], evidential belief function [22,23], certainty factor [24,25], and logistic regression [26][27][28][29].…”

Spatial variation in the frequency and magnitude of mass movement in a semiarid, complex‐terrain agricultural watershed on the Loess Plateau of China

“…Therefore, the mean value of Fr ij is 1. Those values of Fr ij greater than 1 suggest a higher correlation between mass movement occurrence and the causal factor, whereas those of less than 1 refer to a lower correlation (Aditian et al, ).…”

“…To map mass movement susceptibility, the FR method is used to derive the correlation between mass movement occurrence and its causal factors. This method has been widely applied in landslide susceptibility assessment (e.g., Aditian, Kubota, & Shinohara, 2018; W. Chen, Shahabi, et al, 2018;Pradhan & Lee, 2010). The FR is defined as the ratio between the DMMF for each class of a causal factor and that of the entire area.…”

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

“…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]. In particular, hybrid models are increasingly used, such as the rotation forest-based decision trees [46,47], frequency ratio-based ANFIS model [48]…”

“…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]. In particular, hybrid models are increasingly used, such as the rotation forest-based decision trees [46,47], frequency ratio-based ANFIS model [48], bagging-based reduced error pruning trees [49], and multiboost-based support vector machines [50].…”