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Showing 4 results for Shamsi
Micromorphology of the Loess Derived Soils Affected by Land Use Change: A Case Study in Agh-Su Area (Golestan Province)
S. Shamsi Mahmoodabadi, F. Khormali,
Volume 15, Issue 55 (spring 2011)
Abstract
In order to study the effects of different land uses on soil development, a loess hillslope was selected in Agh-Su area, eastern Golestan Province. Six profiles in four land uses including pasture, Quercuse natural forest, Cupressus artificial forest and a cultivated land, were dug and studied. Samples from different horizons were collected for physico-chemical and microscopic analyses. Important physical and chemical attributes such as bulk density (Bd), mean weight diameter (MWD), Organic carbon (SOC), cation exchange capacity (CEC), soil calcium carbonate (CCE) and available P were compared in land uses. Organic matter, CEC and MWD were significantly lower in the cultivated land use. Organic matter content in the forest and pasture area was considerably higher than that of cultivated land use. Soil profile development studies revealed that forest soils were highly developed. Quercus natural forest soils were classified as Calcic Argixerolls. Unlike cultivated soils which showed the minimum development and were classified as Typic Calcixerepts, formation of argillic horizon with dominant speckled b-fabric in the natural forest indicated the high landscape stability. Crystallitic b-fabric of horizons showed the absence of enough leaching of carbonate and the subsequent migration of clay particles. Intense erosion of the surface horizons of cultivated land use resulted in the outcropping of the subsurface carbonate rich horizon preventing soil development. The soils of pasture and Cuprecuse soils had mollic epipedon and were classified as Typic Calcixerolls with moderate development. Micromorphological properties of soils can help consider changes in pedogenic processes occurring under different land covers.
Modeling and Estimating the Dimensions of Stable Alluvial Channels using Soft Calculations
B. Shahinejad, A. Parsaei, A. Haghizadeh, A. Arshia, Z. Shamsi,
Volume 26, Issue 3 (Fall 2022)
Abstract
In this research, soft computational models including multiple adaptive spline regression model (MARS) and data group classification model (GMDH) were used to estimate the geometric dimensions of stable alluvial channels including channel surface width (w), flow depth (h), and longitudinal slope (S) and the results of the developed models were compared with the multilayer neural network (MLP) model. To develop the models, the flow rate parameters (Q), the average particle size in the floor and body (d50) as well as the shear stress (t) as input and the parameters of water surface width (w), flow depth (h), and longitudinal slope (S) were used as output parameters. Soft computing models were developed in two scenarios based on raw parameters and dimensionless form independent and dependent parameters. The results showed that the statistical characteristics in estimating w, the best performance is related to the MARS model, whose statistical indicators of accuracy in the training stage are R2 = 0.902, RMSE=1.666 and in the test phase is R2 = 0.844, RMSE=2.317. In estimating the channel depth, the performance of both GMDH and MARS models is approximately equal, both of which were developed based on the dimensionless form of flow rate as the input variable. The statistical indicators of both models in the training stage are R2 » 0.90, RMSE » 8.15 and in the test phase is R2 » 0.90, RMSE = 7.40. The best performance of the developed models in estimating the longitudinal slope of the channel was related to both MARS and GMDH models, although, in part, the accuracy of the GMDH model with statistical indicators R2 = 0.942, RMSE = 0.0011 in the training phase and R2 = 0.925, RMSE = 0.0014 in the experimental stage is more than the MARS model.

Modeling and Estimating Flow Rate in SMBF Flumes using Soft Computation Models
B. Shahinejad, A. Parsaei, H. Yonesi, Z. Shamsi, A. Arshia,
Volume 26, Issue 4 (Winiter 2023)
Abstract
In the present study, the flow rate in flues containing lateral semi-cylinders (SMBF) was simulated and estimated under free and submerged conditions using back vector machine models (SVM), spin multivariate adaptive regression (MARS), and multilayer artificial neural network (MLPNN) model. In free flow mode, the dimensionless parameters extracted from the dimensional analysis include the ratio of upstream flow to throat width and contraction ratio (throat width to channel width), and in the submerged state, in addition to these two parameters, the depth-to-throat width, and bottom-depth parameters upstream depth were used as input and the two-dimensional form of flow rate was used as the output of the models. The results showed that in free flow mode in the validation stage, the MARS model with statistical indices of R2 = 0.985, RMSE = 0.008, MAPE = 0.87%, and the SVM model with statistical indices of  R2 = 0.971, RMSE = 0.0012, MAPE =1.376%, and MLPNN model with statistical indices of R2 = 0.973,  RMSE = 0.011, MAPE = 1.304% have modeled and predicted the flow rate. In the submerged state, the statistical indices of the developed MARS model were R2 = 0.978, RMSE = 0.018, MAPE = 3.6%, and the statistical indices of the SVM model were R2 = 0.988, RMSE = 0.014, 2%. MAPE = 4, and the statistical indicators of the MLPNN model were R2 = 0.966, RMSE = 0.022, and MAPE = 5.7%. In the development of SVM and MLPNN models, radial kernel and hyperbolic tangent functions were used, respectively.

Comparison of Hydraulic Efficiency of Arched Non-linear Weirs in Plan Using GEP and SVM Neural Networks
M. Majedi Asl, T. Omidpour Alavian, M. Kouhdaragh, V. Shamsi,
Volume 27, Issue 3 (Fall 2023)
Abstract
Non-linear weirs meanwhile economic advantages, have more passing flow capacity than linear weirs. These weirs have higher discharge efficiency with less free height upstream compared to linear weirs by increasing the length of the crown at a certain width. Intelligent algorithms have found a valuable place among researchers due to their great ability to discover complex and hidden relationships between effective independent parameters and dependent parameters, as well as saving money and time. In this research, the performance of support vector machine (SVM) and gene expression programming algorithm (GEP) in predicting the discharge coefficient of arched non-linear weirs was investigated using 243 laboratory data series for the first scenario and 247 laboratory data series for the second scenario. The geometric and hydraulic parameters were used in this research including the water load (HT), weir height (P), total water load ratio (HT/p), arc cycle angle (Ɵ), cycle wall angle (α), and discharge coefficient (Cd). The results of artificial intelligence showed that the combination of parameters (Cd, H_T/p, α, Ɵ) respectively in GEP and SVM algorithms in the training phase related to the first scenario (Labyrinth weir with cycle wall angle 6 degrees) were respectively equal to (R2=0.9811), (RMSE=0.02120), (DC=0.9807), and (R2=0.9896), (RMSE=0.0189), (DC=0.9871) in the second scenario (Labyrinth weir with a cycle wall angle of 12 degrees) it was equal to (R2=0.9770), (RMSE=0.0193), (RMSE=0.9768), and (R2 = 0.9908), (RMSE = 0.0128), (DC = 0.9905), which compared to other combinations has led to the most optimal output that shows the very favorable accuracy of both algorithms in predicting the coefficient the Weir discharge is arched non-linear. The results of the sensitivity analysis indicated that the effective parameter in determining the discharge coefficient of the arched non-linear Weir in GEP and in SVM is the total water load ratio parameter (HT/p). Comparing the results of this research with other researchers revealed that the evaluation indices for GEP and SVM algorithms of this research had better estimates than other researchers.

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