Optimization of gully erosion susceptibility using data-driven statistical combined methods (case study: Toroud-Najar Abad basin)

Arabameri, Alireza; Rezaei, Khalil; Yamani, Mojtaba; Shirani, Kourosh

doi:10.52547/esrj.10.1.18

Optimization of gully erosion susceptibility using data-driven statistical combined methods (case study: Toroud-Najar Abad basin)

Document Type : علمی -پژوهشی

Authors

¹ Ph.D in Geomorphology, Tarbiat Modares Univerity

² Assisstent professor, Faculty of Earth Sciences, Kharazmi University

³ Professor, Faculty of Geography, University of Tehran

⁴ Assistant Professor, Isfahan Province Research Center

10.52547/esrj.10.1.18

Abstract

The first and most important step in reducing damages and management of gully erosion, which leads to waste of water and soil resources, is the gully erosion susceptibility mapping. In present study, the potential of gully erosion in the Toroud-Najarabad basin were evaluated using the uncertainty of the theory of evidence method and linear multivariate regression as well as the combination of the above-mentioned methods. In view of this the gully erosion inventory map was prepared using various resources, and from 218 identified gullies, 70% (154 gullies) and 30% (64 gullies) were selected randomly for modeling and validation respectively. In the next step, 14 controling factors in the gully occurrence in the study area including topographic, geomorphometric, environmental and hydrologic parameters were determined for modeling. The results of multivariate regression model showed that land use parameters, slope and distance from the road had the greatest impact on gully occurrence. Susceptibility maps classified to five susceptibility classes of very low to very high. The area under curve (AUC) and Seed Cell Area Index (SCAI) were used to validate the susceptibility maps. The results of the validation showed that the combined model has a higher area under the curve (AUC) (0.933) toward the evidence-based (0.914) and multivariate regression (0.890) models, as well as, the SCAI value in the combined model showed that very low susceptibility to a very high susceptibility has a downward trend that indicating a proper separation of susceptibility classes in this model. Considering the excellent precision of the combined method proposed in this research, the results of this research can be used by decision makers and local managers to reduce losses and the methodology presented in this study can be used in similar areas to determine the areas that are susceptible to gully erosion.

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