## 7 Conclusions  
This study aims to test the reliability of the MaxEnt machine learning method for mapping the gully erosion susceptibility and to detect the most important geo-environmental factors for the analysis of the erosion susceptibility in a catchment of the Calabria region (South Italy). Twelve predisposing factors were used to build the susceptibility model, including lithology, soil texture, soil bulk density, land use, drainage density, slope gradient, slope aspect, plan curvature, LS, SPI, TPI, and TWI and a gully inventory map.  
The method's predictive capability (goodness-of-fit and prediction skill) was tested by 10-fold cross-validation based on accuracy, kappa coefficient, and AUC parameters. On the basis of the obtained results, the MaxEnt method clearly demonstrated its ability to model gully erosion susceptibility, predicting the areas prone to gullies with excellent performance, as demonstrated by the high values of accuracy (>0.90), of the kappa coefficient (>0.80), and AUC (>0.92). Moreover, the results of the 10 iterations of the MaxEnt algorithm allowed the prediction model to be defined as very stable (robust) and with a low level of uncertainty.  
The probability estimates' mean value and standard were computed for each pixel of the study area by exploiting the 10-fold cross-validation. This procedure allowed the production of gully erosion susceptibility and uncertainty maps. The results showed small variations in the standard deviation values and, consequently, the susceptibility map denotes low levels of uncertainty for each pixel of the map.  
The susceptibility map was classified into five classes: very low, low, moderate, high, and very high susceptibility, showing that high and very high susceptibility classes involved 13.1% of the total area. An excellent spatial agreement between the susceptibility and gully distribution classes was obtained by overlaying the susceptibility and gully erosion inventory maps. Indeed, in this procedure, 92.5% of the gullies were correctly classified, falling into high and very high susceptibility classes.  
The outcomes also revealed that the selection and sensitivity analysis of the predisposing factors represent a key phase for reliable modelling of gully erosion susceptibility.  
Furthermore, all used predisposing factors showed significant importance on the prediction model's performance, as testified by PRI index evaluation results. The predisposing factors that contributed more to the realisation of the gully erosion susceptibility model were SPI, TWI, slope gradient, plan curvature, LS, and lithology.  
Finally, the high predictive capacity of the susceptibility model confirms that the MaxEnt method is able to produce accurate and robust gully erosion susceptibility maps. These maps may provide a valuable tool for local administrators to identify the most susceptible areas to gully erosion that need urgent interventions to mitigate the soil erosion risk.  
Conceptualization, M.C. and F.I.; methodology, M.C.; investigation, M.C. and F.I; data curation, M.C.; writing--original draft preparation, M.C. and F.I; writing--review and editing, M.C. and F.I. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.