Landslide susceptibility mapping has been undertaken using topographic parameters andrnlithologic information in north central Ethhiopia, Debresina area. The area has been one wellrnknown for landslide occurrences throughout its history.rnBefore conducting the GIS analysis, extraction of some of the remote sensing data has beenrnundertaken involving rectification and enhancement of Landsat ETM+ image of May 2000rnfor lithologic units interpretations. Image classification of 5/7, 5/4, Â¾ band combinationrnresulting in 70% accuracy and another classification using the first principal componetsrnwhich accounted for 98 % of the whole bands data was done resulting in 69% accuracy.rnFurther visual interpretation of the lithologic units has been undertaken to produce the finalrnlithologic map interpretation. In addition the digital elevation model (DEM) of the area wasrnobtained at 20m resolution by vectorizing contours from the topographic map of the area tornextract the topographic parameters.rnIn this study two susceptibility mapping methods have been employed: Logistic regressionrnand Artificial Neural Network (ANN). Both have been used to generate the weights tornrepresent the degree of contribution of selected seven parameters: Lithology, Slope, Aspect,rnPlan curvature, Profile curvature and flow accumulation in different platforms thanrngeographic information system (GIS). Preceding to these the class weights of the variousrnparameters were obtained by BSA method. Finally raster calculation of the seven layers ofrnthe parameters was conducted and two susceptibility maps were produced. The weightsrngenerated by ANN signified the contribution of Planar curvature, Aspect and Slope typerntowards landslide occurrence while that of the Logistic regression method signifiedrnLithology and flow accumulation scoring higher in contribution towards landslide occurrencernthan the rest of the parameters. Finally, the outputs have been evaluated using the inventoryrnof slope failure from the same period as those used for training the models and one from thernrecent massive landslide that occurred in the area.