Numerous researches have been published on the application of landslide susceptibility as-sessment models; however, they were only applied in the same areas as the models were originated, the ef-fect of applying the models to other areas than the origin of the models has not been explored. This study is purposed to develop an optimized random forest (RF) model with best ratios of positive-to-negative cells and 10-fold cross-validation for landslide susceptibility mapping (LSM), and then explore its generaliza-tion ability not only in the area where the model is originated but also in area other than the origin of the model. Two typical counties (Fengjie County and Wushan County) in the Three Gorges Reservoir area, China, which have the same terrain and geological conditions, were selected as an example. To begin with, landslide inventory was prepared based on field investigations, satellite images, and historical records, and 1522 landslides were then identified in Fengjie County. 22 landslide-conditioning factors under the influ-ence of topography, geology, environmental conditions, and human activities were prepared. Then, com-bined with 10-fold cross-validation, three typical ratios of positive-to-negative cells, i.e., 1 : 1, 1 : 5, and 1 :10, were adopted for comparative analyses. An optimized RF model (Fengjie-based model) with the best ratios of positive-to-negative cells and 10-fold cross-validation was constructed. Finally, the Fengjie-based model was applied to Fengjie County and Wushan County, and the confusion matrix and area under the receiver operating characteristic (ROC) curve value (AUC) were used to estimate the accuracy. The Fengjie-based model delivered high stability and predictive capability in Fengjie County, indicating a great generalization ability of the model to the area where the model is originated. The LSM in Wushan County generated by the Fengjie-based model had a reasonable reference value, indicating the Fengjie-based model had a great generalization ability in area other than the origin of the model. The Fengjie-based model in this study could be applied in other similar areas/countries with the same terrain and geo-logical conditions, and a LSM may be generated without collecting landslide information for modeling, so as to reduce workload and improve efficiency in practice.