## 1 Introduction  
A global problem that seriously threatens soil and water resources is soil erosion [1; 2; 3]. Gully erosion affects soil productivity, can trigger debris landslides and debris flows [4; 5], and--if sufficiently severe--can cause an undesirable buildup of sediment in waterways, reservoirs, and ponds [6; 7]. Gullies are deep erosional channels on slopes and are commonly a product of ephemeral runoff during periods of heavy rainfall. They provide pathways for water and sediment transport from the upper to lower parts of watersheds. In some catchments, as much as one-third to one-half of the total sediment output is a product of gully erosion [8; 9], and gully erosion constitutes 10 to 94 percent of erosion at the watershed scale [9; 10]. Gully networks also lower the water table in eroded areas, reducing soil moisture and potentially lowering crop yields on the damaged terrain.  
Identifying areas that are susceptible to gully erosion can help land-use managers and planners maintain soil and water resources [10]. Dealing with gullies after they begin to form is difficult and expensive, thus it is better to plan and implement preventative and protective schemes before erosion begins [11].  
Past attempts to identify slopes susceptible to gully erosion have focused on topographic thresholds. However, models that use only topographic thresholds typically fail to identify locations sensitive to gully erosion [12; 13]. They de-emphasize or ignore land-use, hydrological, climatic, and other environmental factors that have key roles in gully erosion, and do not consider the rapid growth of gully systems once they have initiated [14; 15; 16; 17].  
Scientists have used a variety of computational data mining methods and models in natural hazard research, including studies of floods [18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28], wildfire [29], sinkholes [30], droughtiness [31; 32], earthquakes [33; 34], land/ground subsidence [35; 36], groundwater [37; 38; 39; 40; 41; 42; 43; 44], and landslides [45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72]. These methods extract related patterns in historical data to predict future events [73]. Data mining methods used to predict gully erosion include logistic regression (LR) [2; 30; 74; 75; 76; 77], artificial neural network (ANN) [20; 48; 78; 79; 80], random subspace (RS) [48; 62; 81], maximum entropy (ME) [82], artificial neural fuzzy system (ANFIS) [56; 83; 84; 85; 86], support vector machine (SVM) [18; 59; 73], fuzzy analytical network (FAN) [37], multi-criteria decision analysis (MCDA) [87; 88], evidential belief function (EBF) [88; 89], classification and regression tree (CART) [90; 91], random forest (RF) [92; 93; 94; 95; 92], rotation forest (RoF) [95], weights of evidence (WofE) [96], frequency ratio (FR) [28; 97], BFTree for gully headcut [81], boosted regression [24], ADTree, RF-ADTree [73; 76; 98], and naive Bayes tree (NBTree) [67].  
Accurate gully erosion susceptibility maps are required to predict, control, and mitigate gully formation. This need has led researchers to apply and test a wide variety of data mining methods in gully-prone areas. This study uses three hybrid models--Ada-REPTree, Bag-REPTree, and RS-REPTree--to prepare gully erosion hazard zoning maps for the Rabat Turk watershed in northwestern Iran and to compare the results with those obtained using other models. The study area has an arid to semi-arid climate, a limited vegetation cover, and easily eroded bedrock, all of which make it susceptible to gully erosion.