%0 Journal Article %A Salimi, N %A FatemiAghda, M %A Teshnehlab, M %A Sharafi, Y %T Landslide Hazard Zonation in Taleghan watershed by using intelligent systems (Gaussian radial basis function and multilayer perceptron artificial neural networks) %J Journal of Engineering Geology %V 10 %N 3 %U http://jeg.khu.ac.ir/article-1-2591-en.html %R 10.18869/acadpub.jeg.10.3.3601 %D 2017 %K Landslide hazard zonation, Taleghan, intelligent systems, artificial neural network, artificial neural networks, Perceptron, RBF, MLPLandslide hazard zonation, Taleghan, intelligent systems, artificial neural network, artificial neural networks, Perceptro, %X Landslides are natural hazards that make a lot of economical and life losses every year. Landslide hazard zonation maps can help to reduce these damages. Taleghan watershed is one the susceptible basin to landslide that has been studied. In this paper, landslide hazard zonation of the study area is performed at a scale of 1:50,000. To achieve this aim, layers information such as landslides distribution, slope, aspect, geology (lithology), distance from the faults and distance from rivers using artificial neural network-based Radial Basis Function (RBF) and perceptron neural network (MLP), has been studied. Principal of RBF method is similar to perceptron neural network (MLP), which its ability somewhat has been identified up to now and there are several structural differences between these two neural networks. The final results showed that the maps obtained from both methods are acceptable but the MLP method has a higher accuracy than the RBF method. %> http://jeg.khu.ac.ir/article-1-2591-en.pdf %P 3601-3626 %& 3601 %! %9 Original Research %L A-10-515-1 %+ %G eng %@ 2228-6837 %[ 2017