Corrigendum to “Assessing the performance of GIS- based machine learning models with different accuracy measures for determining susceptibility to gully erosion” [Sci. Total Environ. 664 (2019) 1117–1132]

Comparison of differences in resolution and sources of controlling factors for gully erosion susceptibility mapping

Abstract Gully erosion has been identified as an important soil degradation process and sediment source, especially in arid and semiarid areas. Thus, it is useful to identify the spatial occurrence of this form of water erosion in the landscape and the most vulnerable areas. In this study, we explored the effects of different pixel sizes on some controlling factors extracted from a digital elevation model and remote sensing data when producing a gully erosion susceptibility map (GESM) of Ekbatan Dam Basin, Hamadan, Iran. An inventory map of the gully landforms was prepared based on global positioning system routes of the gullies, extensive field surveys, and visual interpretations of satell…

Assessing the performance of GIS- based machine learning models with different accuracy measures for determining susceptibility to gully erosion

Assessing the performance of GIS- based machine learning models withdifferent accuracy measures for determining susceptibility togully erosionYounes Garosia, Mohsen Sheklabadia,⁎, Christian Conoscentib, Hamid Reza Pourghasemic,d, Kristof Van Ooste,faFaculty of Agriculture, Department of Soil Science, Bu Ali Sina University, Ahmadi Roshan Avenue, 6517838695 Hamedan, IranbDepartment of Earth and Sea Sciences (DISTEM), University of Palermo, Via Archirafi22, 90123 Palermo, ItalycCollege of Marine Sciences and Engineering, Nanjing Normal University, Nanjing, 210023, ChinadDepartment of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, IraneA- Fo…