Testing the Reliability of Maximum Entropy Method for Mapping Gully Erosion Susceptibility in a Stream Catchment of Calabria Region (South Italy)

Abstract

Gully erosion poses severe problems for land degradation in several areas worldwide. This study aims to evaluate the accuracy and robustness of the maximum entropy (MaxEnt) method for assessing gully erosion susceptibility. We selected the catchment of the Mesima stream as the test site, which is situated in the southwest sector of the Calabria region (South Italy). An inventory map of gully erosion was realised and 12 predisposing factors, such as lithology, soil texture, soil bulk density, land use, drainage network, slope gradient, aspect, length–slope (LS), plan curvature, stream power index (SPI), topographic position index (TPI), and topographic wetness index (TWI), were selected to implement the dataset in the MaxEnt method. The accuracy and uncertainty of the method were tested by 10-fold cross-validation based on accuracy, kappa coefficient, and receiver operating characteristic curve (ROC) and related area under curve (AUC). The dataset was randomly divided into 10 equal-sized groups (folds). Nine folds (90% of the selected dataset) were used to train the model. Instead, the remaining fold (10% of the dataset) was used for testing the model. This process was repeated 10 times (equal to the number of the folds) and each fold was used only once as the validation data. The average of 10 repeated processes was performed to generate the susceptibility map. In addition, this procedure allowed the reliability of the susceptibility map to be assessed, in terms of variables, importance and role of predisposing factors selected, prediction ability, and accuracy in the assessed probabilities for each pixel of the map. In addition to exploiting the 10-fold cross-validation, the mean value and standard deviation for the probability estimates of each pixel were computed and reported in the susceptibility and uncertainty map. The results showed that the MaxEnt method has high values of accuracy (>0.90), of the kappa coefficient (>0.80), and AUC (>0.92). Furthermore, the achieved findings showed that the capacity of the method used for mapping gully erosion susceptibility is quite robust when the training and testing sets are changed through the 10-fold cross-validation technique.

Keywords

• gully erosion-prone areas
• forecast models
• machine-learning algorithm
• k-fold cross-validation
• Mediterranean area