||Gully erosion susceptibility mapping is a fundamental tool for land-use planning aimed atmitigating land degradation.
However, the capabilities of some state-of-the-art data-mining models for developing accurate maps of
gully erosion susceptibility have not yet been fully investigated. This study assessed and compared the performance
of two different types of data-mining models for accurately mapping gully erosion susceptibility at a regional
scale in Chavar, Ilam, Iran. The two methods evaluated were: Certainty Factor (CF), a bivariate statistical
model; andMaximumEntropy (ME), an advancedmachine learningmodel. Several geographic and environmental
factors that can contribute to gully erosionwere considered as predictor variables of gully erosion susceptibility.
Based on an existing differential GPS survey inventory of gully erosion, a total of 63 eroded gullies were
spatially randomly split in a 70:30 ratio for use inmodel calibration and validation, respectively. Accuracy assessments
completed with the receiver operating characteristic curve method showed that the ME-based regional
gully susceptibility map has an area under the curve (AUC) value of 88.6% whereas the CF-based map has an
AUC of 81.8%. According to jackknife tests that were used to investigate the relative importance of predictor variables,
aspect, distance to river, lithology and land use are themost influential factors for the spatial distribution of gully erosion susceptibility in this region of Iran. The gully erosion susceptibility maps produced in this study
could be useful tools for landmanagers and engineers taskedwith road development, urbanization and other future