_Applied_  
_sciences_  
GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models  
**[PERSON] 1,2, [PERSON] 3, [PERSON] 3, [PERSON] 4, [PERSON] 5, [PERSON] 6, [PERSON] 7, [PERSON] 8,9, [PERSON] 10, [PERSON] 11, [PERSON] 12, [PERSON] 13,*, [PERSON] 14 and [PERSON] 15,16,*  
Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam; [EMAIL_ADDRESS]  
Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam  
Department of Watershed Management Engineering, College of Natural Resources, Tarbiat Modares University, Tehran, P.O. Box 14115-111, Iran; [EMAIL_ADDRESS] (S.J. ); [EMAIL_ADDRESS] (M.A.) College of Geology & Environment, Xi'an University of Science and Technology, Xi'an 710054, China; [EMAIL_ADDRESS]  
Department of Forestry, Range and Watershed Management, Faculty of Agriculture and Natural Resources, Yasouji University, Yasouji 75918-74934, Iran; [EMAIL_ADDRESS]  
Department of Rangeland and Watershed Management, Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan 87317-53153, Iran; [EMAIL_ADDRESS]  
Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Samandaj 66177-15175, Iran; [EMAIL_ADDRESS]  
Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran; [EMAIL_ADDRESS]  
Board Member of Department of Zrebar Lake Environmental Research, Kurdistan Studies Institute, University of Kurdistan, Sanandaj 66177-15175, Iran  
Department of Earth Sciences, Simon Fraser University, Burnaby, BC V5A 1S6, Canada; [PERSON]ESS]  
Research Institute of Forests and Rangelands, Agricultural Research, Education, and Extension Organization (AREEO), Tehran P.O. Box 64414-356, Iran; [EMAIL_ADDRESS]  
Data Mining Laboratory, Department of Engineering, College of Farabi, University of Tehran, Tehran 37181-17469, Iran; [EMAIL_ADDRESS]  
Institute of Research and Development, Duy Tam University, Da Nang 550000, Vietnam  
Department of Geoinformation, Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia; [EMAIL_ADDRESS]  
Geoscience Platform Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124 Gwahak-ro, Yuseong-gu, Daejeon 34132, Korea  
Department of Geophysical Exploration, Korea University of Science and Technology, 217 Gajeong-ro, Yuseong-gu, Daejeon 34113, Korea  
Correspondence: [EMAIL_ADDRESS] (B.T.P. ); [EMAIL_ADDRESS] (S.L. ); Tel. : +82-42-8683-057 (S.L)  
Received: 25 January 2020; Accepted: 12 March 2020; Published: 17 March 2020  
Gully erosion destroys agricultural and domestic grazing land in many countries, especially those with arid and semi-arid climates and easily eroded rocks and soils. It also generates large amounts of sediment that can adversely impact downstream river channels. The main objective of this research is to accurately detect and predict areas prone to gully erosion. In this paper, we couple hybrid models of a commonly used base classifier (reduced pruning error tree, REPTree) with AdaBoost (AB), bagging (Bag), and random subspace (RS) algorithms to create gully erosion susceptibility maps for a sub-basin of the Shoor River watershed in northwestern Iran. We compare the performance of these models in terms of their ability to predict gully erosion and discuss their potential use in other arid and semi-arid areas. Our database comprises 242 gully erosion locations, which we randomly divided into training and testing sets with a ratio of 70/30. Based on expert knowledge and analysis of aerial photographs and satellite images, we selected 12 conditioning factors for gully erosion.