## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**AUC**} & \multirow{2}{*}{**SE**} & \multicolumn{2}{c}{**95\% CI**} \\ \cline{3-4}  & & & **Lower Bound** & **Upper Bound** \\ \hline
**REPTree** & **0.800** & 0.0383 & 0.725 & 0.862 \\ \hline AB-REPTree & 0.805 & 0.0368 & 0.731 & 0.866 \\ Bag-REPTree & 0.841 & 0.0329 & 0.771 & 0.896 \\ RS-REPTree & 0.860 & 0.0315 & 0.793 & 0.912 \\ LR & 0.824 & 0.0350 & 0.751 & 0.882 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: ROC curve using the validation dataset. and site-specific and that their performances depend heavily on the datasets that are trained and built upon. Although it is difficult to directly compare the results of this study with those reported from other regions, we suggest that our ensemble models perform better than the generalized linear model (AUC = 0.71), boosted regression tree (AUC = 0.84), multivariate adaptive regression spline (AUC = 0.83), and ANN (AUC = 0.84) models used by [PERSON] et al. [104]; the certainty factor model (AUC = 0.82) used by [PERSON] et al. [82]; and the [PERSON]'s linear discriminant analysis (AUC = 0.76), logistic model tree (AUC = 0.77), and NBT (AUC = 0.78) models of [PERSON] et al. [125]. In contrast, however, our models were outperformed by the maximum entropy (AUC = 0.88, 0.90) models used by [PERSON] et al. (2019) and [PERSON] et al. [107]; BFTree and its ensembles (bagging and RS) (AUC = 0.92) used by [PERSON] et al. [81]; and the multivariate additive regression splines (AUC = 0.91), SVM (AUC = 0.88), and FR (AUC = 0.96) models employed by [PERSON] et al. [126]. Again, these different results are attributable to local differences in the environments in which the models were used. Our field survey indicated that gullies in the study area are located along tributaries near the main river in the Rabat Turk study area. Erosion is initiated by focusing of runoff along these tributaries, gradual gully retrogression, and piping above gully heads. Gullies on the east side of the river have lower slopes than those on the west side of the river, perhaps because there is little vegetation in the former areas. There is also more upslope area for gully development on the west side of the river, allowing for more flow with the gully system. Our results are in agreement with the findings of [PERSON] et al. [127] and [PERSON] [128], who argued that gullies are mainly formed through extreme runoff related to slope-area relations. The gully erosion susceptibility map of the study area obtained using the RS-REPTree ensemble model accurately predicts observed gullies along the main river and its tributaries. Despite the improved prediction performance provided by ensemble models, the difficulty associated with proper parameter tuning still restricts their development and application. In this study, we manually tuned the parameters of the ensemble methods through a trial-and-error process [129; 130]. There are, however, several optimization techniques (e.g., metaheuristic optimization algorithms) that can significantly speed up the process of model building [131; 132]. Nevertheless, ensemble models are easy to develop within open-source WEKA software and do not require advanced programming knowledge. They can be applied to types of environmental research that involve datasets with a number of geo-environmental variables and a set of presence/absence locations of the phenomenon being modeled. Such datasets can be generated with automated GIS techniques from accessible geospatial data (e.g., DEM, soil, lithology, and meteorological records).