Neural network-based model for assessing failure potential of highway slopes in the Alishan, Taiwan Area: Pre- and post-earthquake investigation

Monitoring and Predicting Slope Instability: A Review of Current Practices From a Mining Perspective

“…In a study conducted by Hwang, Guevarra and Yu [29], general slope factors were analyzed and classified using a decision tree algorithm to evaluate the validity of a Korean slope database comprised of6,828 slope observations. In another study, Lin, Chang, Wu and Juang [30] created an empirical model to estimate failure potential of highway slopes using failure attributes specific to highway slopes in the Alishan, Taiwan area before and after the 1999 Chi-Chi, Taiwan earthquake. Beyond those listed, there are many more studies that have used neural networks to assess slope instability.…”

An RVM-Based Model for Assessing the Failure Probability of Slopes along the Jinsha River, Close to the Wudongde Dam Site, China

“…Based on the review of previous literature [13,16,17,25] and field surveys, seven factors that were identified as causative and triggering factors for landslide activity in the study area were selected to predict the failure potential of slopes. They are lithology, slope angle, slope aspect, slope height, slope structure, distance from faults, and land use.…”

“…The slope angle was measured from the slope profile that was derived from the digital elevation model (DEM). Note that a slope angle of 10 • was taken as the threshold because very few slopes less than that angle have failed [16]. The slope angle of the study area was divided into five classes (Table 1) …”

“…They are lithology, slope angle, slope aspect, slope height, slope structure, distance from faults, and land use. This research selected a single slope as a computing unit [16], and values of the influence factors were derived for each slope in the database. Table 1 presents the factors and their classes used in this study.…”

“…With the development of science and technology, soft computing techniques, such as data mining and artificial intelligence, have also been widely used in geological hazard assessment. Examples of these approaches include artificial neural networks [14][15][16][17], genetic algorithms [18,19], decision trees [20,21], and support vector machines [22][23][24][25]. However predictions provided by these methods are not probabilistic.…”