Identification of morphometric features of alluvial fan and basins in predicting the erosion levels using ANN

The aim of this study was to investigate the morphometry of alluvial fans and predict the soil erosion located in the vicinity of the Sabzevar, Daruneh, Khaf, and Jangal faults in northeastern Iran. First, the morphometric properties of each alluvial fan were determined. In addition to determining their influence on erosion, principal component analysis (PCA) was used to select the most important morphometric factors affecting erosion. Then, the data regarding the important parameters were input into adaptive neural-fuzzy networks (ANFIS) to predict erosion rates and determine the effect of faults on alluvial morphometry. The asymmetric factor (A(f)), hypsometric integral (H-i), and basin shape (BS) indicate that most of the sub-basins are tectonically active. The results of the PCA revealed that the most important parameters affecting erosion were A(f), P-f, L-f, R-f, V-f, P-b, A(b), LC, L-b, D-d, and the geological unit. The ANFIS method showed that among the soil erosion prediction models, the FCM hybrid model had the highest accuracy. It is concluded that morphometric features can be used to predict the erosion processes in the basin. Thus, the Sabzevar, Daruneh, Khaf, and Jangal faults have been more influential on morphometry than other tectonic factors. The results showed that faults were active and affected the morphometry of the watershed.

Automated summary

The aim of this study was to investigate the morphometry of alluvial fans and predict soil erosion in northeastern Iran. The results showed that the Sabzevar, Daruneh, Khaf, and Jangal faults have a significant influence on alluvial morphometry and erosion processes.