Landslide susceptibility assessment is an important tool for disaster management and development activities. Shikoku Island in the southwest Japan is one of the most landslide prone areas due to heavy typhoon rainfall, complex geology and the presence of mountainous areas and low topographic features (valleys).Yanase and Naka Catchments of Shikoku Island in Japan were chosen as a study area. The objective of this study is to apply Frequency Ratio Densisty (FRD), Logistic Regression (LR) and Weights of Evidence (WoE) models in a GIS environment to prepare the landslide susceptibility maps of this area and select the best one for future infrastructure and landuse planning. Data layers including slope, aspect, profile curvature, plan curvature, lithology, land use, distance from river, distance from fault and annual rainfall were used in this study. In FR method, two models were attempted but the FRD model was found slightly better in its performance. In case of LR method, two models, one with equal proportion and the other with unequal proportion of landslide and non-landslide points were applied and the one with equal proportions was chosen based on its highest performance. A total of five landslide susceptibility maps(LSMs) were produced using FR, LR and WoE models resulting two, two and one LSMs respectively. However, one best model was chosen from the FR and LR methods based on the highest area under the curve (AUC) of the receiver operating characteristic (ROC) curves. This reduced the total number of landslide susceptibility maps to three with the success rates of 86.7%, 86.8% and 80.7% from FRD, LR and WoE models respectively. For validation purpose, all landslides were overlaid over the three landslide susceptibility maps and the percentage of landslides in each susceptibility class was calculated. The percentages of landslides that fall in the high and very high susceptibility classes of FRD, LR and WoE models showed 82%, 84% and 78% respectively. This showed that the LR model with equal proportions of landslides and non-landslide points was slightly better than FRD and WoE models in predicting the probability of future landslide occurrence.