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Articles by S.M. Kashefipour
Total Records ( 8 ) for S.M. Kashefipour
  N. Javaheri , M. Ghomeshi and S.M. Kashefipour
  In this study the basic theories of development of the meanders in rivers are described and categorised and the role of sediment balance on the morphological changes in these types of rivers is investigated using the fuzzy method. A supervised clustering fuzzy algorithm was applied to determine the sediment balance in a meandering river. The capability of the fuzzy method in recognition of the sediment transport trend was first checked using two analytical problems and the approved algorithm was then applied to recognise the trend of sediment transport and balance for Karoon River, the largest river in Iran. The predicted results obtained from the fuzzy model were compared with the sediment balance results for three sediment measuring sites along a 140 km reach of Karoon. This research has shown that the sediment balance along the research area was negative before construction of Karoon dam and as a result the bank erosion was continued at that period and after dam operation the sediment transport capacity of the river has decreased and now the river is in sedimentation conditions. The result of this research study was also that sedimentation occurs at the outer banks for this situation and as a result the river sinuosity index decreased and radius curvature of river increased.
  N. Javaheri , M. Ghomeshi and S.M. Kashefipour
  .
  S.M. Kashefipour , S. Broomand Nasab and B. Sohrabi
  In the present research the water-yield relationship has been determined for the cotton (Si-Ocra varity) under sprinkler irrigation. The research was carried out with five irrigation treatments at three replications for two consecutive years (2004 and 2005). The amount of water for irrigation was calculated and applied based on the class-A pan evaporation. The treatments were 120% (T0), 100% (T1), 70% (T2), 40% (T3) and 0% (T4) of the accumulative pan evaporation. The two years average data showed that the maximum Marginal Water Use Efficiency (MWUE) was obtained for T1 with a value of 4.18 kg ha-1 mm-1. The water production function as an average for two years was obtained as: YW = -1.522W2 +152.34W -312.16 with a correlation coefficient of 0.96 where, W and YW are the applied water (cm) and yield (kg ha-1), respectively. This equation shows that the maximum yield could be obtained with 500 mm/year of applied water. However, for the regions with no restriction in water irrigation but limited agricultural lands the optimum applied water was obtained 430 mm/year and at this amount of water application the net income would be maximum. For the adverse conditions in which, water availability is a restricted factor the optimum applied water was calculated to be 346 mm/year.
  S.M. Kashefipour , S. Broomand Nasab and S. Taheri Ghannad
  This study describes the relationship of irrigation water application, water stress, CWSI and crop yield for the spring corn, hybrid SC704. Irrigation was scheduled using accumulative pan evaporation. Five treatments including 50, 70, 90, 110 and 130 mm based on the accumulative pan evaporation were used as the main plots. Crop Water Stress Indexes (CWSI) were calculated using two different methods described by Idso and Jackson in the literature. A good linear equation was developed using the least square method between the grain yield and the crop water stress index (Jackson method) in the form of Ys = -10.295CWSIj+13.196 with a correlation coefficient of R2 = 0.97. Another equation was also developed, in which the relative humidity, the net receiving solar radiation, the depleted soil moisture from the root zone and the wind speed were related to the canopy-air temperature difference.
  A. Roshanfekr , S.M. Kashefipour and N. Jafarzadeh
  In the present research, the dissolved lead is modeled using a linked 1D and 2D hydro-environmental model. Details are given for the governing equations and solution methods used in these numerical models, together with a discussion of a new development in dissolved heavy metals modeling using varied reaction coefficients in the model. It is found that pH and EC play an essential role in adsorption and desorption of heavy metals by the particles in solution. Therefore, in this study it has been tried to find the best relationship between pH and EC with the reaction coefficient. Relatively close agreement between predicted results and field measured dissolved lead concentrations were obtained for different varied reaction coefficients using the linked 1 and 2D model. Finally, the best relationships for the reaction coefficients for dissolved lead were introduced and the results were successfully compared with the corresponding measured values.
  A.A. Tavakoly Zadeh and S.M. Kashefipour
  The study describes the application of the Artificial Neural Networks (ANNs) to predict local scour downstream of a grade-control structure. Four important dimensionless parameters, including: the ratio of the maximum scour depth to the height of structure (s/z), the ratio of distance of maximum scour from structure to z (XS/z), the ratio of distance of maximum deposit mound to z (SD/z) and the ratio of maximum height of dune to z (hd/z) have been modeled using ANNs. The scour measurements available in the literature were used to establish and verify the ANNs models. The final models for each scour variable parameters have been compared favorably with the recent experimental formulations published in the literature, describing the downstream scour of grade-control structures.
  N.G. Ebrahimi , M. Fathi- Moghadam , S.M. Kashefipour , M. Saneie and K. Ebrahimi
  Vegetation roughness coefficients are the main parameters used to determine river flow characteristics and are known to depend on the flow condition (depth and velocity) as well as vegetation condition (type and density). Flume experiments were conducted to investigate the variation of roughness coefficients with flow conditions and vegetation density for submerged vegetation in river bed, banks and flood plains. Artificial plastic plants, for a length of 0.2 m, were laid on the floor of a 14 m long and variable slope flume facilitated in the Hydraulics Laboratory of the Soil Conservation and Watershed Management Research Institute, Tehran-Iran for this study. The Manning's n values were estimated for different slopes, discharges, flow depth and vegetation densities. The results reveal that the Manning roughness coefficient (n) increases as vegetation density increases, while it decreases when the flow depth and velocity increase. Significant variation of the Manning's n value with flow and vegetation conditions urges the consideration of the flow and vegetation conditions in estimation of the Manning roughness coefficient (n).
  R. Tareghian and S.M. Kashefipour
  This study presents the development of Artificial Neural Networks (ANNs) and Fuzzy Logic (FL) models for prediction of daily reservoir inflow. Furthermore, a Linear Regression (LR) model was also developed as a traditional method for flood forecasting. To illustrate the applicability and capability of the ANNs and FL models, the Dez reservoir, located in the south-west of Iran, was used as a case study. The results demonstrated that ANNs model can predict the reservoir inflow for 1-day-ahead, especially for training pattern better than the FL and LR models. It was found that the accuracy of ANNs model predictions decreased for flood forecasting more than 1-day ahead (e.g., 2, 3, or 4 days ahead), whereas the results obtained from the FL and LR models showed better correlation with the corresponding measured values in this conditions. One of the main findings of this research was that the fuzzy logic model generally underestimated the flood even for, whereas the other two considered models predicted the flood discharge relatively good. The peak value of the hydrograph, which is very important from the flood hazard viewpoint, was estimated good by the ANNs and LR models for the short period (1-day ahead), with the error being 3, 4.5 and 26% for the ANNs, LR and FL models, respectively. For the long periods (e.g., 3-days ahead) the flood discharge was predicted by the LR and FL models slightly better than the ANNs model.
 
 
 
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