This study presents a landslide susceptibility assessment for the Caspian forest using frequency ratio and index of entropy models within geographical information system (GIS). Firstly, the landslide locations were identified in the study area from interpretation of aerial photographs and multiple field surveys. 72 cases (70%) out of 103 detected landslides were randomly selected for modelling and the remaining 31 (30%) cases were used for the model validation. The landslide conditioning factors, including slope degree, slope aspect, altitude, lithology, rainfall, distance to faults, distance to streams, plan curvature, topographic wetness index (TWI), stream power index (SPI), sediment transport index (STI), normalized difference vegetation index (NDVI), forest plant community, crown density, and timber volume were extracted from the spatial database. Using these factors, landslide susceptibility and weights of each factor were analyzed by frequency ratio and index of entropy models. Results showed that the high and very high susceptibility classes cover nearly 50% of the study area. For verification, the ROC (receiver operating characteristic) curves were drawn and the areas under the curve (AUC) calculated. The verification results revealed that the index of entropy model (AUC=75.59%) is slightly better in prediction than frequency ratio model (AUC=72.68%). The interpretation of the susceptibility map indicated that NDVI, altitude, and rainfall play major roles in landslide occurrence and distribution in the study area. The landslide susceptibility maps produced from this study could assist planners and engineers for reorganizing and planning of future road construction and timber harvesting operations.