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.
Suma Mohamadpur; Hamed Rouhani; Hojat Ghorbani Vaghei; Seyed Morteza Seyedian; Abulhasan Fath Abadi
Abstract
In many semi-arid regions of Iran, soil erosion has turned into a serious environmental problem affecting land productivity, nutrient loss, water quality, and fresh water ecosystems. Rates of soil loss differ according to erosion type and land degradation processes. Rill erosion is commonly observed ...
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In many semi-arid regions of Iran, soil erosion has turned into a serious environmental problem affecting land productivity, nutrient loss, water quality, and fresh water ecosystems. Rates of soil loss differ according to erosion type and land degradation processes. Rill erosion is commonly observed when rainstorms occur on steep slopes and sediment transport in rill flows exhibits the characteristics of non-equilibrium transport. In this paper, sediment concentration of rill flow is estimated by adaptive neuro-fuzzy inference system (ANFIS). A series of mathematical equations and parameters affecting rill hydrodynamics and soil detachment were used for well-defined rill sediment concentration. A series of filed experiments were performed to evaluate the model. The stepwise method was used to select the most important and effective input variables from measured input parameters of soil properties, topographic and vegetation attributes affecting sediment concentration of rill flow. Based on the stepwise procedure, the most significant parameters in the model predications were steep slope, vegetation percentage, clay percentage, and shear stress parameters. The values of sediment concentration simulated by the model were in agreement with observed values with Coefficient of Correlation (R2), Root Mean Square Error (RMSE) and Mean Bias Error (MBE) of 0.697, 30.5 and 1.0, respectively. The results of the investigation shows that the data-driven ANFIS modeling approach can be a powerful alternative technique for correctly estimating rill sediment concentration.
