This study presents an integration of fuzzy sets theory with analytic hierarchy process (AHP) to model landslide hazard. The approach involves developing expert knowledge from existing landslide datasets which are used for standardizing digital terrain attributes, a pairwise comparison method for the elicitation of attribute weights, and their subsequent aggregation through weighted linear comb...

Landslide databases and input parameters used for modeling landslide hazard often contain imprecisions and uncertainties inherent in the decision-making process. Dealing with imprecision and uncertainty requires techniques that go beyond classical logic. In this paper, methods of fuzzy k -means classification were used to assign digital terrain attributes to continuous landform classes whereas ...

The increasing availability of remotely sensed data offers a new opportunity to address landslide hazard assessment at larger spatial scales. A prototype global satellite-based landslide hazard algorithm has been developed to identify areas that may experience landslide activity. This system combines a calculation of static landslide susceptibility with satellite-derived rainfall estimates and ...

Statistical classification techniques complemented by GIS yield good results in predicting landslide hazard/ susceptibility. In this work, several well-known classification methods are applied to data from distinct alpine areas in Vorarlberg, Austria. It is shown that kernel methods (Support Vector Machines – SVM – and Gaussian Processes) outperform classic techniques for this task. As a furthe...

Landslides are usually initiated under complex geological conditions. It is of great significance to find out the optimal combination of predisposing factors and create an accurate landslide susceptibility map based on them. In this paper, the Information Value Model was modified to make the Modified Information Value (MIV) Model, and together with GIS (Geographical Information System) and AUC ...

A landslide susceptibility analysis is performed by means of Artificial Neural Network (ANN) and Cluster Analysis (CA). This kind of analysis is aimed at using ANNs to model the complex non linear relationships between mass movements and conditioning factors for susceptibility zonation, in order to identify unstable areas. The proposed method adopts CA to improve the selection of training, vali...

We present the results of the application of a recently proposed model to determine landslide hazard. The model predicts where landslides will occur, how frequently they will occur, and how large they will be in a given area. For the Collazzone area, in the central Italian Apennines, we prepared a multi-temporal inventory map through the interpretation of multiple sets of aerial photographs tak...

Shallow landslides, triggered by extreme rainfall, are a significant hazard in mountainous landscapes. The hazard posed by shallow landslides depends on the availability and strength of colluvial material in landslide source areas and the frequency and intensity of extreme rainfall events. Here we investigate how the time taken to accumulate colluvium affects landslide triggering rate in the So...