تحقيقات جغرافيايی (Mar 2018)
Comparison of Gully Erosion Susceptibility Mapping Using Weight of Evidence and Frequency Ratio Models at Sanganeh Kalat Basin
Abstract
Gully erosion is the most advanced type of water erosion in watersheds that produces large volumes of sediment that and cause a lot of damage. Thus mapping susceptibility to gully erosion and identification of factors can help managers and decision-makers to reduce the risk of erosion. The objective of the present study is to assess the capability of weights-of-evidence (WofE) and frequency ratio (FR) models for spatial prediction of gully erosion susceptibility and characterizing susceptibility conditions at Sanganeh Kalat Basin. At first, a gully erosion inventory map is prepared through extensive field study, then raster maps of the variables affecting the Gully Erosion (lithology, land use, distance from river, slope degree, slope direction, plan curvature, topographic wetness index, drainage density and altitude) in a database and Geographic Information System (GIS) was created. In total, of the 46 gullies which have been identified, 32 (70 %) cases are random algorithm selected to build gully susceptibility models, while the remaining 14 (30 %) cases are used to validate the models. The effectiveness of gully erosion susceptibility assessment via GIS-based models depends on appropriate selection of the conditioning factors, which play an important role in gully erosion. Learning vector quantization (LVQ), one of the supervised neural network methods, is employed in order to estimate variable importance. Finally, validation of the gully dataset which has not been utilized during the spatial modeling process is applied to validate the gully susceptibility maps. The receiver operating characteristic curves for each gully susceptibility map are drawn, and the areas under the curves (AUC) are calculated. The results show that the gully erosion susceptibility map produced by the frequency ratio model (AUC = 86.32 %) functions well in prediction compared to the wofe model (AUC = 73.49 %). Furthermore, LVQ results reveal that drainage density, slope degree, distance from river and topographic wetness index are the most effective factors
