Mapping soil erosion susceptibility: a comparison of neural networks and fuzzy-AHP techniques

The purpose of this research was to model areas prone to erosion in the Gol-Mehran catchment in southern Iran. For this purpose, the soil erosion map was determined using membership functions and analytic hierarchy process (AHP) determined the soil erosion map. Additionally, using the self-organizing map (SOM) and principal component analysis (PCA) methods, the most crucial parameters affecting gully erosion were extracted. Finally, soil erosion was predicted using a multilayer perceptron (MLP) and radial basis function. The results of the fuzzy AHP method with all data and the selected data with SOM and PCA demonstrated that areas located in the center of the region were prone to gully erosion. The results of this research also demonstrated that urban lands have expanded significantly, while vegetation has decreased from 1990 to 2019, which has had a significant impact on soil erosion. The results also showed that the MLP model, with R2 = 0.97, could accurately predict soil erosion.