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Showing 27 results for Parsa
A New Method for Estimating Hydraulic Parameters by Measuring Soil Moisture in the Field
Sh. Zand-Parsa, S. Parvizi, A. R. Sepaskhah, A. A. Kamgar Haghighi,
Volume 22, Issue 1 (Spring 2018)
Abstract
In this study, the values of moisture and soil temperature were estimated at different depths and times under unsteady conditions by solving the Richards’ equation in an explicit finite difference method provided in Visual Studio C#. For the estimation of soil hydraulic parameters, including av and nv (coefficients of van Genuchten’s equation) and Ks (saturated hydraulic conductivity), soil moisture and temperature at different depths were measured by TDR probes and the stability apparatus, respectively. The objective function [equal to Root Mean Square Error (RMSE)] was minimized by the optimization of a parameter separately, using the Newton-Raphson method, while, the other parameters were considered as the constant values. Then, by replacing the optimized value of this parameter, the same was done for other parameters. The procedure of optimization was iterated until reaching minor changes to the objective function. The results showed that soil hydraulic parameters (coefficients of van Genuchten’s equation) could be optimized by using the SWCT (Soil Water Content and Temperature) model with measuring the soil water content at different depths and meteorological parameters including the  minimum and maximum temperature,, air vapor pressure, rainfall and solar radiation. Finally, the measured values of soil moisture and temperature were compared to the depth of 70cm in spring, summer, and autumn of 2015. The values of  the  normalized RMSE of soil water content were 0.090, 0.096 and 0.056 at the  soil depths of 5, 35 and 70 cm, respectively, while the values of the normalized RSME of soil temperatures were 2.000, 1.175 and 1.5 oC at these depths, respectively. In this research, the values of soil hydraulic parameters were compared with other previous models in a wider range of soil moisture varying from saturation to air dry condition, as more preferred in soil researches.

Estimation of Wheat Water Productivity and Water Use Efficiency (Shiraz Cultivar) under Different Amounts of Deficit Irrigation and Nitrogen Deficiencies
Sh. Zand-Parsa, F. Ghasemi Saadat Abadi, M. Mahbod, A. R. Sepaskhah,
Volume 24, Issue 2 (Summer 2020)
Abstract
Due to the limited water resources and growing population, food security and environmental protection have become a global problem. Increasing water productivity of agricultural products is one of the main solutions to cope with the difficulties. By optimizing applied water and nitrogen fertilizer, the pollution of groundwater could be deceased and the water productivity could be increased. The aim of this research was to determine the relationships between water productivity (IRWP) and water use efficiency (WUE) and different amounts of applied water (irrigation + rain fed) and nitrogen (applied and residual). This study was conducted on wheat (Triticum aestivum L., cv. Shiraz) in Shiraz University School of Agriculture, based on a split-plot design with three replications, in 2009-2010 and 2010-2011 periods. Irrigation treatments varied from zero to 120% of full irrigation depth, and nitrogen fertilizer treatments varied from zero to 138 kg ha-1 under basin irrigation system. The experimental data of the first and second years were used for the calibration and validation of the proposed relationships, respectively. The calibrated equations using the dimensionless ratios of irrigation depth plus rainfall, actual evapotranspiration and nitrogen fertilizer plus soil residual nitrogen to their amounts in full irrigation and maximum fertilizer amounts were appropriate for the estimation of water productivity and water use efficiency. The values of the determination coefficient (R2) for water productivity and water use efficiency (0.88 and 0.93, respectively), and the values of their normalized root mean square error (NRMSE) (0.2 and 0.13, respectively) showed a good accuracy for the estimation of IRWP and WUE.

Estimation of Actual Evapotranspiration, Water productivity, and Irrigation Efficiency of Wheat Fields in Surface and Sprinkler Irrigation Systems Using Remote Sensing
F. Ghasemi-Saadat Abadi, S. Zand-Parsa, M. Mahbod,
Volume 25, Issue 4 (Winiter 2022)
Abstract
In arid and semi-arid regions, water resource management and optimization of applying irrigation water are particularly important. For optimization of applying irrigation water, the estimated values of actual evapotranspiration are necessary for avoiding excessive or inadequate applying water. The estimation of actual crop evapotranspiration is not possible in large areas using the traditional methods. Hence, it is recommended to use remote sensing algorithms for these areas. In this research, actual evapotranspiration of wheat fields was estimated using METRIC algorithm (Mapping EvapoTranspiration at high Resolution with Internalized Calibration), using ground-based meteorological data and satellite images of Landsat8 at the Faculty of Agriculture, Shiraz University, in 2016-2018. In the process of METRIC execution, cold pixels are located in well-irrigated wheat fields where there is no water stress and maximum crop evapotranspiration occurred. The estimated maximum values of evapotranspiration using the METRIC algorithm were validated favorably using the obtained values by the AquaCrop model with NRMSE (Normalized Root Mean Square Errors) equal to 0.12. Finally, the values of water productivity (grain yield per unit volume of evapotranspiration) and irrigation efficiency were estimated using the values of predicted actual evapotranspiration using remote sensing technique. The values of measured irrigation water and produced wheat grain yield in 179 ha were estimated at 0.86 kg m-3 and 75%, respectively.

Simulation of Pore Water Pressure in the Body of Earthen Dams during Construction Using Combining Meta-Heuristic Algorithms and ANFIS
H. Hakimi Khansar, A. Hosseinzadeh Dalir, J. Parsa, J. Shiri,
Volume 26, Issue 2 (ُSummer 2022)
Abstract
Accurate prediction of pore water pressure in the body of earth dams during construction with accurate methods is one of the most important components in managing the stability of earth dams. The main objective of this research is to develop hybrid models based on fuzzy neural inference systems and meta-heuristic optimization algorithms. In this regard, the fuzzy neural inference system and optimizing meta-heuristic algorithms including genetic algorithms (GA), particle swarm optimization algorithm (PSO), differential evolution algorithm (DE), ant colony optimization algorithm (ACOR), harmony search algorithm (HS), imperialist competitive algorithm (ICA), firefly algorithm (FA), and grey wolf optimizer algorithm (GWO) were used to improve training system. Three features including fill level, dam construction time, and reservoir level (dewatering) obtained from the dam instrumentation were selected as the inputs of hybrid models. The results showed that the hybrid model of the genetic algorithm in the test period had the best performance compared to other optimization algorithms with values of R2, RMSE, NRMSE, and MAE equal to 0.9540, 0.0866, 0.1232, and 0.0345, respectively. Also, ANFIS-GA, ANFIS-PSO, ANFIS-ICA, and ANFIS-HS hybrid algorithms performed better than ANFIS-GWO, ANFIS-FA, ANFIS-ACORE, and ANFIS-DE in improving ANFIS network training and predicting pore water pressure in the body earthen dams at the time of construction.

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.

Numerical Hydraulic and Hydrodynamic Investigation of the Flow through Multiple Rectangular Lopac Gates with Flow 3D Software
M. Karamdokht Bahbahani, M. Sajjadi, J. Ahadiyan, A. Parsaie,
Volume 28, Issue 1 (Spring 2024)
Abstract
One of the structures for regulating the water level in the irrigation and drainage ducts is the lopac gates, which are proposed as a structure for regulating and controlling the flow level. In this study, a new design of this type of structure has been proposed in which the gates are placed next to each other in pairs, and they are called multiple lopac gates. The objective of this research is to investigate the effective hydraulic parameters of the proposed structure and compare it in a case where a gate is used under the same conditions. All the simulations were modeled with 3 amounts of opening 30, 45, and 60 degrees and at 3 flow rates of 20, 40, and 60 liters per second and using Flow3d software, in these simulations, the number of mesh cells is 1000000 and RNG turbulence model is used.  The results showed that the maximum shear stress was reduced by an average of 38% compared to the single gate mode in most tests at different openings and flow rates using multiple lopac gates, and the largest amount of this reduction was related to the opening of 45 degrees, and the flow rate is 40 liters per second with a value of 76%. Also, the forces acting on the gate at different flow rates and openings will be reduced by 150% on average. In the qualitative investigation of flow vortices, the investigations also showed that vortex range, length, and strength are reduced compared to the single gate mode when two gates are used, and the number of vortices increases compared to when a single valve.

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