Somayeh Movahedi; aboalhasan fathabadi; null null; Ali Heshmatpour
Abstract
In this study using Frequency Ratio (FR), Statistical Index (SI), Weights Of Evidence(WOF), Logistic Regression (LR), Random Forest (RF) models the probability of gully formation was calculated in Aytamar watershed and susceptibility maps was prepared. First the thematic maps of 13 gully conditioning ...
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In this study using Frequency Ratio (FR), Statistical Index (SI), Weights Of Evidence(WOF), Logistic Regression (LR), Random Forest (RF) models the probability of gully formation was calculated in Aytamar watershed and susceptibility maps was prepared. First the thematic maps of 13 gully conditioning factors including lithological formations, distance to faults, faults density, altitude, slope-length, slope angle, slope aspect, plan curvature, profile curvature, distance to roads, land use, distance to rivers, stream power index and topographic wetness index was prepared. Then landslide inventory map was combined with each gully conditioning factor and all models weights and parameters were calculated. Area under curve for test data was calculated as 0.74, 0.78, 0.75, 0.86 and 0.96 for Frequency Ratio (FR), Statistical Index (SI), Weights Of Evidence(WOF), Logistic Regression (LR), Random Forest (RF) models, respectively. Random forest, Frequency Ratio and Logistic Regression have the least the area of high susceptibility zone, respectively. With respect three validation criteria multivariate methods including Random Forest and Logistic Regression had the best performance among all models.
aboalhasan fathabadi; Ali Salajegheh; hamid pezeshk; Aliakbar Nazari Samani; hamed rouhani
Abstract
In order to manage and implement conservational activities in watershed successfully, it is necessary to determine the sediment sources. In recent years, sediment fingerpering techniques have been used for estimating sediment sources contribution. With respect to small source samples, having many answer ...
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In order to manage and implement conservational activities in watershed successfully, it is necessary to determine the sediment sources. In recent years, sediment fingerpering techniques have been used for estimating sediment sources contribution. With respect to small source samples, having many answer as a result of over fitting, there are some uncertainties in estimated sources contribution. In this study, the uncertainty associated with the multivariate mixing model was estimated using Monte Carlo simulation and GLUE approach in Zidasht-Fashandak sub- watershed. The sediment and source samples were taken in the study area and then, 54 geochemistry and three organic characteristics were measured. 17 elements were also selected as optimum tracer composition using Kruskal–Wallis H-test and multivariate discriminate analysis. Meanwhile, sources contribution were estimated using multivariate mixing models. Results showed higher contribution of sub-surface sources than the surface resources. Also, the distance between lower and upper limits for all sources and resolutely uncertainty bands were high.
