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“…To the density of geological boundary, as its values increases, the W i values also increases that means more landslides occurred. The maximum W i value 3.31 is obtained for the class with densest geological boundary (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27) followed by a value of 2.82 in the lower geological density class (10)(11)(12)(13)(14)(15). The greater density of geological boundaries indicates a plane of weakness or zone of discontinuity that leads to instability of rock bodies.…”
Section: Modeling Results With the Information Value (Inv)mentioning
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“…To the density of geological boundary, as its values increases, the W i values also increases that means more landslides occurred. The maximum W i value 3.31 is obtained for the class with densest geological boundary (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27) followed by a value of 2.82 in the lower geological density class (10)(11)(12)(13)(14)(15). The greater density of geological boundaries indicates a plane of weakness or zone of discontinuity that leads to instability of rock bodies.…”
Section: Modeling Results With the Information Value (Inv)mentioning
“…Apart from the models what we have compared in this study, there are other more sophisticated approaches are mentioned in the recent literature [74]. Indeed, the comparison of different machine learning models and their performance issues has become a trend in the LSM studies [11,74] However, assessment of model performance not only depends on the methods, but also it is mainly decided by the quality of the collected data [56]. For example, a random forest (RF) model applied using historical 222 landslides in Jiangxi, China with the help of 25 m DEM returned only a ROC value of 0.75 [75].…”
Section: Discussionmentioning
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“…An ROC curve closer to the top-left corner indicates a better model [73,74]. The closer the curve comes to the 45-degree diagonal, the less accurate [75,76]. AUC is a measure of the accuracy of the models: 1 is optimal and 0.5 is poor [50,51].…”
Section: Validation Criteriamentioning
“…Nevertheless, in most cases, machine learning approaches performed better compared to other conventional analytical and expert opinion based methods (Zhou et al, 2018). For instance, artificial neural network (Chen et al, 2017b;Wang et al, 2019), random forest (Pourghasemi and Rahmati, 2018;Dou et al, 2019), support vector machine (Xu et al, 2012;Kumar et al, 2017), K-nearest neighbor (KNN) (Miloš Marjanovic et al, 2009;Chang et al, 2011), logistic regression (Hong et al, 2015;Zhou et al, 2018) 1 Corresponding author and decision tree (Pradhan, 2013) models have been extensively used for analyzing landslide susceptibility and achieved high prediction accuracies. Most of the aforementioned studies confirmed the central role of geological factors (lithology, structure, and weathering), topographical factors (slope, elevation, aspect, etc.…”
Section: Introductionmentioning