## 5 Conclusions  
Gully erosion is an advanced stage of water erosion and sediment production that can transfer large volumes of sediment into stream channels, resulting in environmental damage. It is a common problem in arid and semi-arid landscapes, and therefore, prediction and mapping of areas susceptible to gully erosion are of interest to soil scientists, natural resource authorities, and land managers. Accordingly, researchers have used a variety of machine learning methods to understand the causes of gully erosion and to produce reliable erosion susceptibility maps [133].  
We addressed this problem by studying gully erosion in a sub-basin of the Shoor River watershed in Isfahan Province (Iran), which has a semi-arid climate and a human-impacted landscape. We used 12 conditioning factors tested by the information gain ratio method, and REPTree coupled with the AB, BA, and RS ensemble learning methods to model gully erosion and produce gully erosion susceptibility maps. The following are key conclusions of our study:  
(1) Rainfall, elevation, and river density are the most important factors for gully erosion in the study area. Most gully erosion sites are located in areas of lower rainfall and lower elevation.  
(2) REPTree and all its ensembles yielded a high goodness-of-fit and prediction accuracy during the modeling process, but the ensemble RS-REPTree performed best. RS decreased over-fitting and noise in the training datasets, which resulted in better prediction. It successfully predicted gully erosion locations and allowed us to produce an accurate gully erosion susceptibility map of the study area.  
(3) Modeling gully erosion is a complicated task, with many uncertainties. The proposed machine learning model is an easy-to-use, inexpensive decision-making tool that can supplement expensive field surveys. It also provides managers with guidance on what further information might be needed to provide a more accurate map of gully erosion.  
(4) Gully erosion susceptibility maps are essential products for hazard analysis and management. We recommend our proposed ensemble RS-REPTree model for predicting gully erosion in other semi-arid and arid areas. However, the performance of this model depends on the quality of the data used.  
(5) We recommend further research on other hybrid data mining methods, as well as ensemble boosting algorithms with REPTree. We also recommend further sensitivity analysis of gully erosion conditioning factors.  
V.-[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] contributed equally to the work. [PERSON], [PERSON], [PERSON], and [PERSON] collected field data and conducted the gully erosion mapping and analysis. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] wrote the manuscript. V.-[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] provided critical comments in planning this paper and edited the manuscript. All the authors discussed the results and edited the manuscript. All authors have read and agreed to the published version of the manuscript. This research was supported by the Basic Research Project of the Korea Institute of Geoscience, Mineral Resources (KIGAM) funded by the Minister of Science and ICT. The authors declare no conflict of interest.