## 4 Discussion  
Obtaining reliable map of gully erosion susceptibility remains yet a challenge for managers, land use planners, and engineers. To address this challenge, researchers are proposing new models and testing them in different gully-prone regions around the world. In this paper, we propose and evaluate three ensembles of the REPTree model for gully erosion susceptibility mapping. The modeling process is based on an investigation of the relationships between spatial locations of gullies in the Rabat Turk watershed and a suite of different geo-environmental factors. We demonstrate that rainfall, elevation, river density, distance to rivers, land-use, and lithology are important factors for gully erosion in the study area. In contrast, plan curvature, slope, NDVI, aspect, and distance to roads are the less important. An examination of the literature suggests that conditioning factors for gully erosion are area-specific and cannot be reliably extrapolated to other regions. For example, [PERSON] et al. [121] identified land-use as the most important factor in their study areas, whereas [PERSON] et al. [122] and [PERSON] et al. [92] reported that distance from rivers is the most important factor in their studies. Furthermore, the slope factor, which we and [PERSON] et al. [122] ranked as a relatively unimportant factor, was among the most effective factors identified by [PERSON] et al. [97]. These differences call for further research on controls of gully erosion in different landscapes. The ensemble learning techniques used in this study (AB, bagging, and RS) improved the goodness-of-fit and prediction performance of REPTree. Among these techniques, random subspace outperformed the other two techniques in improving both the training and validation of the base REPTree model. The RS ensemble learning technique performed better than the other techniques in decreasing the variance, bias, and noise of the modeling process, and protected the models from over-fitting. The superiority of the RS ensemble learning technique stems from the use of random subspaces for aggregating the base classifiers, which results in better performance compared to the original feature space [112]. In addition, the base classifier works better using smaller subspaces, as shown by [PERSON] et al. [123]. The literature includes numerous successful applications of RS ensemble learning techniques for predicting different types of natural hazards. For example, [PERSON] et al. [76] showed that the naive Bayes tree performed better when used in combination with the RS technique for landslide modeling, and [PERSON] et al. [62] demonstrated that the RS technique improved the performance of the alternating decision tree base classifier. Our results suggest that the Bagging technique is the second-best ensemble learning method for improving REPTree performance, which is in line with previous findings. For example, [PERSON] et al. [124] reported that bagging, used in combination with the j48 decision tree, has higher predictive capacity than the single j48 and AB-j48 models alone. In another study, [PERSON] et al. [58] reported that the functional tree (FT) model with bagging outperforming the AB-FT method. Although our study is the first to use REPTree in combination with ensemble learning techniques for gully erosion modeling, this approach has been used by [PERSON] et al. [123] for predicting landslides. They ranked the ensemble models in terms of prediction capability, from best to worst, to be: BA-REPTree (AUC = 0.872), rotation forest REPTree (AUC = 0.872), RSRETree (AUC = 0.864), and MultiBoost REPTree (AUC = 0.855). The differences in their results and ours suggest that the techniques are case