Landslide vulnerability and risk assessment for multi-hazard scenarios using airborne laser scanning data (LiDAR)

GIS-based predictive models for regional-scale landslide susceptibility assessment and risk mapping along road corridors

“…On the contrary, quantifying, in mathematical terms, the landslide risk can be very complicated, due to several aspects, related to the complexity in assessing the temporal probability of a specific landslide event with given intensity (hazard) and the probability of damaging a given element at risk, i.e. vulnerability (Glade 2003;Uzielli et al 2008;Pellicani, Van Westen, Spilotro 2014;Abdulwahid & Pradhan 2016). Nevertheless, for a specific type of landslide mechanism and element at risk, a number of procedures for assessing and mapping risk have been proposed in the literature.…”

Landslide Susceptibility Assessment Using Frequency Ratio Technique with Iterative Random Sampling

“…Damage caused by natural hazards has been increasing due largely to residential expansion and population growth in many countries in the world [1][2][3]. Thus, government employees and researchers in those countries have been believed to be vulnerable to natural disasters and hence have studied hazard warning systems and hazard risk analyses in order to prevent the loss of lives and minimize the damage to property.…”

The Influence of Different Knowledge-Driven Methods on Landslide Susceptibility Mapping: A Case Study in the Changbai Mountain Area, Northeast China

“…The success accuracies of the landslide susceptibility maps produced by the ICM, RS-ICM, AHP-ICM, and EWM-ICM methods were 0.931, 0.939, 0.912, and 0.883, respectively, with prediction accuracy rates of 0.926, 0.927, 0.917, and 0.878 for the ICM, RS-ICM, AHP-ICM, and EWM-ICM, respectively. Hence, it can be concluded that the four models used in this study gave close results, with the RS-ICM exhibiting the best performance in landslide susceptibility mapping.Entropy 2019, 21, 372 2 of 24 involving land use management as an efficient approach to reduce property damage and economic loss in landslide-prone areas [1,[6][7][8][9]. The outcome maps would be useful for general planned development activities and disaster management in the future, such as choosing new urban areas and infrastructural activities, as well as for environmental protection.Landslide susceptibility maps can be obtained using both qualitative (inventory-based and knowledge-driven methods) or quantitative approaches (data-driven methods and physically based models) [4,[10][11][12][13][14][15][16][17].Landslide inventory-based techniques, as a prelude to all other methods, include the collection of past landslide data, construction of databases, and production of susceptibility maps based on those data [18].…”

“…In bivariate statistical analysis, the weights of the landslide conditioning factors are assigned based on landslide density using different methods-including frequency ratio (FR) [13,15,[20][21][22], the information content model (ICM) [23,24], weight of evidence (WoE) [16], certainty factors (CF) [25], favorability functions (FF) [26], and the likelihood ratio model (LRM) [27]. The multivariate statistical methods evaluate the combined relationship between a dependent variable (landslide occurrence) and a series of independent variables (landslide controlling factors), and the most popular methods to analyze the resulting matrix include logistic regression (LR) [6,13,[28][29][30][31][32][33], discriminant analysis (DA) [34,35], random forest (RF) [36][37][38] and active learning statistical analysis, such as the artificial neural networks (ANNs) [3,6,[39][40][41][42].Physically based methods, such as deterministic techniques, are based on mathematical modeling of the physical mechanisms controlling slope failure [43][44][45][46][47][48][49]. However, it is reported that the methods are only applicable over large areas when the geological and geomorphological conditions are fairly homogeneous and the landslide types are simple [17].Moreover, several studies have used two or more models to produce landslide susceptibi...…”

“…Entropy 2019, 21, 372 2 of 24 involving land use management as an efficient approach to reduce property damage and economic loss in landslide-prone areas [1,[6][7][8][9]. The outcome maps would be useful for general planned development activities and disaster management in the future, such as choosing new urban areas and infrastructural activities, as well as for environmental protection.…”