Predicting Fault Locations based on Morphometric Features of Alluvial Fans and Basins using Artificial Neural Networks

Abstract

The aim of this study is to investigate the morphometry of alluvial fans located in the vicinity of the Sabzevar and Sang-Sefid faults in northeastern Iran to determine their influence on erosion Principal component analysis (PCA) was used to select the most important morphometric factors affecting erosion. The data regarding the important parameters were input into adaptive neural-fuzzy networks (ANFIS) to predict erosion rates. The asymmetric factor (Af), hypsometric integral (Hi), 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 Af, Pf, Lf, Rf, Vf, Pb, Ab, LC, Lb, Dd, 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.