This research presents the results of the GIS-based statistical models for generation of landslide susceptibility mapping using geographic information system (GIS) and remote-sensing data for Cameron Highlands area in Malaysia. Ten factors including slope, aspect, soil, lithology, NDVI, land cover, distance to drainage, precipitation, distance to fault, and distance to road were extracted from ...

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...

In northern parts of Iran such as the Alborz Mountain belt, frequent landslides occur due to a combination of climate and geologic conditions with high tectonic activities. This results in millions of dollars of financial damages annually excluding casualties and unrecoverable resources. This paper evaluates the landslide susceptible areas in Central Alborz using the probabilistic frequency rat...

The aim of the study is to confirm the importance of discriminate different types of slope movements for a better landslide susceptibility evaluation. The study was applied to the sample area of Calhandriz (11.3 km2) in the area North of Lisbon. Sixty shallow translational slides, 23 deeper translational movements and 19 rotational movements were selected for statistical analysis. Landslide sus...

In the framework of the European Soil Thematic Strategy, and the associated preparation of a directive on the protection and sustainable use of soil, landslides were recognized as a soil threat requiring specific strategies for risk assessment and management. The criteria for harmonized risk area delineation proposed by the Soil Information Working Group (SIWG) of the European Soil Bureau Netwo...

In this paper, we propose a multiple kernel relevance vector machine (RVM) method based on the adaptive cloud particle swarm optimization (PSO) algorithm to map landslide susceptibility in the low hill area of Sichuan Province, China. In the multi-kernel structure, the kernel selection problem can be solved by adjusting the kernel weight, which determines the single kernel contribution of the f...

Recurrence of rain-induced landslides is controlled by the repetition of rain events. Therefore, rainfall induced landslide hazard analysis is more complicated than a conventional rainfall frequency analysis, and requires multi-stage procedures. It requires a susceptibility analysis to divide a region into successive classes at the first stage. Then, the relationship between the probability of ...

[1] We quantify the effect of spatial patterns in climatological rainfall on shallow landslide susceptibility by forcing a physically based model of slope stability (SHALSTAB) with the rainfall pattern produced by a high-resolution atmospheric model (MM5) over the western Olympic Mountains of Washington State. Our results suggest that for two small basins in the Olympics, 10 km–scale variations...

Preparation of landslide susceptibility maps is important for engineering geologists and geomorphologists. However, due to complex nature of landslides, producing a reliable susceptibility map is not easy. In this paper, the weights-of-evidence model (a Bayesian probability model) was applied to the task of evaluating landslide susceptibility using GIS. Using landslide location and a spatial da...