Showing 3 results for S. Marofi
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.
