Finally, for the training dataset, the results reveal that the LADT-Bagging model acquire the largest AUC value (0.980), smallest standard error (SE) (0.0134), narrowest 95% confidence interval (CI) (0.920–0.999), highest accuracy value (AV) (91.03%), highest specificity (94.44%), highest sensitivity (88.10%), highest F-measure (0.9115), lowest MAE (0.2016), lowest RMSE (0.2653), and highest Kappa (0.8205). Besides, heuristic tests were also applied to identify the appropriate values of each model's parameters in order to obtain the best programmer. Third, the LADT-Bagging and FPA-Bagging models were built by the integration of the LogitBoost alternating decision trees (LADT) with the Bagging ensemble and Forest by Penalizing Attributes (FPA) with the Bagging ensemble, respectively. Then, a multicollinearity analysis using TOL and VIF parameters and Pearson's correlation coefficient methods were applied to verify the multicollinearity and correlation between these factors. Second, the Support Vector Machines classifier (SVMC) approach was adapted to analyze the predictive capability of the landslide predisposing factors in each method. First of all, we prepared a geospatial database in the study area, including 79 landslide points that were divided into a training and validating dataset and 14 landslide conditioning factors. The major target of this study is to design two novel hybrid integration artificial intelligent models, which are denoted as LADT-Bagging and FPA-Bagging, for modeling landslide susceptibility in the Youfanggou district (China).