Showing 7 results for Marofi
H Tabari, S Marofi, H Zare Abiane, R Amiri Chayjan, M Sharifi, A.m Akhondali,
Volume 13, Issue 50 (winter 2010)
Abstract
In mountainous basins, snow water equivalent is usually used to evaluate water resources related to snow. In this research, based on the observed data, the snow depth and its water equivalent was studied through application of non-linear regression, artificial neural network as well as optimization of network's parameters with genetic algorithm. To this end, the estimated values by artificial neural network, neural network-genetic algorithm combined method and regression method were compared with the observed data. The field measurement were carried out in the Samsami basin in February 2006. Correlation coefficient (r) mean square error (MSE) and mean absolute error (MAE) were used to evaluate efficiency of the various models of artificial neural networks and nonlinear regression models. The results showed that artificial neural network and genetic algorithm combined methods were suitable to estimate snow water equivalent. In general, among the methods used, neural network-genetic algorithm combined method presented the best result (r= 0.84, MSE= 0.041 and MAE= 0.051). Of the parameters considered, elevation from sea level is the most important and effective to estimate snow water equivalent.
M. Shadmani, S. Marofi,
Volume 15, Issue 55 (spring 2011)
Abstract
In this research, based on the observed data of Class A pan evaporation and application of non-linear regression (NLR), artificial neural network (ANN), neuro-fuzzy (NF) as well as Stephens-Stewart (SS) methods daily evaporation of Kerman region was evaluated. In the cases of NLR, ANN and NF methods, the input variables were air temperature (T), air pressure, relative humidity (RH), solar radiation (SR) and wind speed (U2) which were used in various combinations to estimate daily pan evaporation (Ep) defined as output variable. Performance of the methods was evaluated by comparing the observed and estimated data, using determination coefficient (R2), root mean square error (RMSE) and mean absolute error (MAE). Based on the observed data at Kerman meteorological station, the monthly and annual average evaporation values of the region were 272 and 3263 mm, respectively. The results of this study indicated that NF method is the most suitable method to estimate daily Class A pan evaporation. The statistics criteria of this model which is constituted based on the 5 input parameters were R2 = 0.85, RMSE=1.61 and MAE= 1.24 mm day-1. The sensitivity analysis of NF model revealed that the estimated EP is more sensitive to T and U2 (as the input variables), respectively. Due to weak accuracy of SS method, a new modification step of the model was also developed based on the SR and T in order to have a more exact daily evaporation estimation of the region. However, the result of the modified model was not acceptable
S. Marofi, N. Parsafar, Gh. Rahimi, F. Dashti,
Volume 16, Issue 61 (fall 2012)
Abstract
In this study, a completely randomized experiment was designed with four irrigation treatments and three replicates. The irrigation programs were raw wastewater, treated wastewater, a combination of 50% raw wastewater and 50% potable water and a combination of 50% treated wastewater and 50% potable water. The experiments were run within a greenhouse. The lysimeters were built up on September 2009 and they were filled with two layers of soil. The upper (0-30 cm in depth) and lower (30-70 cm in depth) layers were sandy loam and sandy clay loam, respectively. A total of eight watering programs with an interval of elevens-day were applied. After each irrigation program, intake wastewater and drainage water of each Lysimeter was sampled in order to analyse the transport of heavy metals (Cu, Zn, Fe and Mn, Ni, Cd and Pb). Results showed that the effect of water quality was significant on percentage of transport of heavy metals. The lowest transport percentage of heavy metals belonged to raw wastewater treatment. Also, the highest percentage of transport of Cu, Zn, Fe, Ni and Pb belonged to the combination of 50% raw wastewater and 50% potable water. In most cases, we observed that the transport percentage of these elements increased by continuing the irrigation
N. Parsafar , S. Marofi,
Volume 16, Issue 62 (Winte - 2013 2013)
Abstract
In this research, we estimated soil shallow depths temperatures using regression methods (Linear and Polynomial). The soil temperatures at soil depths (5, 10, 20, 30, 50 and 100 cm) were correlated with meteorological parameters. For this purpose, temperature data of Hamedan station (in the period 1992-2005) were employed. Soil temperature data were measured on a daily basis at 3 PM, 9 PM and 3 AM. MS Excel was used for deriving the regressions between soil temperature and meteorological parameters (air temperature, relative humidity and sunshine hours). The results showed that the highest coefficient of determination (R2) of the linear regression was between soil temperature in 20 cm soil depth and air temperature at 3 AM (R2= 98.15%) and the lowest value in 100 cm soil depth at 3PM (R2= 83.96%). Also, the highest R2 of non-linear regression was observed between soil temperature in 10 cm soil depth and air temperature at 3 AM (R2= 98.45%) and lowest value in 100 cm soil depth at 3PM (R2= 84.11%). The results showed that the highest and lowest values of R2 of linear relations between meteorological parameters (relative humidity and sunshine hours) and soil temperature were observed in 10 cm soil depth (at 3 AM) and in 100 cm soil depth, respectively. Correlations of soil temperature with air temperature were greater than those with the other two parameters. Moreover, R2 values of non- linear relation were higher than linear relation.
N. Parsafar, S. Marofi ,
Volume 17, Issue 66 (winter 2014)
Abstract
In this study, a completely randomized experiment was designed with five irrigation treatments and three replicates. The irrigation programs were raw wastewater (T1), treated wastewater (T2), a combination of 50% raw wastewater and 50% fresh water (T3), a combination of 50% treated wastewater and 50% fresh water (T4), and fresh water (T5). The experiments were run within a greenhouse. The lysimeters were built up in September 2009 and they were filled with a two layer soil. The upper (30 cm) and lower (40 cm) layers were sandy loam and sandy clay loam, respectively. The results showed that the effects of watering treatments on transfer coefficients of heavy metals from soil to shoots (except Cd) and tubers of potato (except Zn and Cu) were significant (p <0.01). Maximum and minimum transfer coefficients of heavy metals were observed in the (T1) and (T5) treatments, respectively. Also, the transfer coefficients of Cd from soil to shoots were lower than tubers. In the case of Zn, Cu and Pb, transfer coefficients from soil to tubers were lower than shoots. In this study, the maximum transfer coefficients to shoots were Cd (0.331-0.463), Zn (0.383-0.230), Cu (0.173-0.386) and Pb (0.003-0.057), respectively. Maximum transfer coefficients toward tubers (except T5) were Cd (0.439-0.572), Cu (0.081-0.138), Zn (0.170-0.217) and Pb (0-0.017), respectively. The combination of wastewater and fresh water use in short-term irrigation might be feasible, but a heavy metal monitoring program is necessary.
S. Azadi, H. Nozari, S. Marofi, Dr. B. Ghanbarian,
Volume 26, Issue 1 (Spring 2022)
Abstract
In the present study, a model was developed using a system dynamics approach to simulate and optimize the profitability of crops of the Jofeyr (Isargaran) Irrigation and Drainage Network located in Khuzestan Province. To validate the results, the statistical indicators of root mean square error (RMSE), standard error (SE), mean biased error (MBE), and determination coefficient (R2) were used. To validate the simulation results of the benefit-cost ratio, the values of these indicators were obtained 0.25, 0.19, 0.005, and 0.96, respectively. Then, to determine the optimal cultivated area of the network and increase the profitability, the cropping pattern was determined both non-stepwise and stepwise in 2013 to 2017 cropping years. In the non-stepwise, the cultivated area of each crop changed from zero to 2 times of current situation. In stepwise, due to social and cultural conditions of inhabitants, this change was slow and 10% of the current situation every year. The analysis of the results showed the success of the model in optimizing and achieving the desired goals and the total benefit-cost ratio increased in all years both non-stepwise and stepwise. For example, in 2017 compared to 2016, production costs decreased by 7.1 percent and sales prices increased by 5.8 percent, and increased the benefit-cost in 2017 compared to the previous year. The results showed that the present model has good accuracy in simulating and optimizing the irrigation network, its cropping pattern, and defining other scenarios.
S. Azadi, H. Nozari, S. Marofi, B. Ghanbarian,
Volume 27, Issue 3 (Fall 2023)
Abstract
One of the strategies for agricultural development is the optimal use of irrigation and drainage networks, which will lead to higher productivity and environmental protection. The present study used the system dynamics approach to develop a model for simulating the cultivated area of the Shahid Chamran irrigation and drainage network located in Khuzestan province by considering environmental issues. Limit test and sensitivity analysis were used for model validation. The results showed the proper performance of the model and the logical relationship between its parameters. Also, the cropping pattern of the network was determined in two modes of non-stepwise and stepwise changes to determine the optimal cultivated area of the Shahid Chamran network with environmental objectives and minimize the amount of salt from drains. The results showed that the amount of optimized output salt from the network has decreased in both non-stepwise and stepwise changes compared to the existing situation in the region. The total output salt in the current situation, from 2013 to 2017, was obtained at 2799, 2649, 2749, 2298, and 2004 tons.day-1, respectively, in the stepwise changes, are 2739, 2546, 2644, 2223, and 1952 tons.day-1, and finally, in the non-stepwise changes, are 2363, 2309, 2481, 2151, and 1912 tons.day-1. The results showed that the non-stepwise changes due to considered limitations have been more successful in reducing output salt than the stepwise changes. The analysis of the results showed the model's success in optimizing and achieving the desired goals. The results showed that the present model has good accuracy in simulating and optimizing the irrigation network, cropping pattern, and defining other scenarios.
