Logistic regression and artificial neural network models for mapping of regional-scale landslide susceptibility in volcanic mountains of West Java (Indonesia)

Análisis comparativo de susceptibilidad de erosión y evaluación de incertidumbre en la subcuenca del Río Claro, Costa Rica

“…La explicación a esta diferencia puede deberse, entre otros posibles factores, a las diferencias existentes en la importancia relativa de las variables explicativas entre los dos modelos, como se muestra en los Cuadros 2 y 5. Estas diferencias también se manifiestan en Ngadisih et al, (2016) y Lee et al, (2016).…”

“…Para ello, se han empleado comparativamente dos modelos de predicción, regresión logística y redes neuronales artificiales. Este último presentó una mayor capacidad predictiva en términos de la función ROC, resultado que comparten la mayoría de estudios (Nefeslioglu et al, 2008;Ngadisih, et al, 2016;Pradhan y Lee, 2010). Esta evidencia empírica, unida al hecho de que el segundo modelo es un método no-paramétrico y que, por tanto, permite flexibilizar determinados supuestos de partida con respecto a las variables explicativas, fortalece su prevalencia sobre el modelo de regresión logística.…”

Entropy-Based Hybrid Integration of Random Forest and Support Vector Machine for Landslide Susceptibility Analysis

“…Any study related to landslides depends on the accuracy of the landslide inventory data collected and mapped [13,14]. This data can be prepared by satellite image processing, historical documentation and reports, and field surveys of landslide locations [15]. The occurrence of landslides in a region is influenced by various topographical, geological, hydrological, and anthropological factors.…”

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].…”