In this study, modeling of discharge was performed in compound open channels with non-prismatic floodplain (CCNPF) using soft computation models including Multivariate Adaptive Regression Splines (MARS) and Group Method of Data Handling (GMDH) and then their results were compared with the multilayer perceptron neural networks (MLPNN). In addition to the total discharge, the discharge separation between the floodplain and main channel was modeled and predicted. The parameters of relative roughness coefficient, the relative area of flow cross-section, relative hydraulic radius, bed slope, the relative width of water surface, relative depth, convergence or divergence angle, relative longitudinal distance as inputs, and discharge were considered as models output. The results demonstrated that the statistical indices of MARS, GMDH, and MLPNN models in the testing stage are R2 = 0.962(RMSE = 0.003), 0.930(RMSE = 0.004), and 0.933(RMSE = 0.004) respectively. Examination of statistical error indices o shows that all of the developed models have the appropriate accuracy to estimate the flow discharge in CCNPF. Examination of the structure of developed GMDH and MARS models demonstrated that the parameters of relative: roughness, area, hydraulic radius, flow aspect ratio, depth, and angle of convergence or divergence of floodplain have the greatest impact on modeling and estimation of discharge.
Screens are one of the recent energy dissipator structures that can be used downstream of small hydraulic structures. In this study, screens were used horizontally at the brinks of the vertical drop with downstream smooth and rough bed to investigate the energy loss of drop. Experiments were performed on two porosities of screens, a relative critical depth of 0.13–0.39 and a median size of 1.9 cm aggregates. The results showed that for a relative critical depth of more than 0.3 in a vertical drop equipped with a screen with a rough bed, the drop length with respect to smooth bed increases. Compared to applying a Type I stilling basin, a vertical drop equipped with a screen with downstream smooth and rough bed reduces the drop length by approximately 50%. Although a rough bed increases air entrainment, it has no effect on the energy loss and pool depth of a vertical drop equipped with a horizontal screen with smooth bed. The use of horizontal screens at the brinks of the vertical drop causes maximum energy loss in the downstream of drop. Equations were provided to estimate the flow parameters with a R2 value of more than 0.925 and a normalized root mean square error of less than 0.04.
Dam reservoirs usually play the most important role in the water resources systems and their optimal utilization in economic and social terms is indispensable. Sedimentation in dam's reservoirs is one of the destructive phenomena which leads to reduction of useful volume of reservoirs and also damages the installations and disturbs their functions. Area reduction method is the most common experimental method to measure the sediment distribution in reservoirs. In this method, reservoirs are geometrically divided into four types. Parameters obtained for each type are based on limited number of chosen reservoirs and consequently the results lead to large scale errors for accuracy of this method. Therefore choosing appropriate parameters can help us to have more acceptable accuracy. In this study, first based on area reduction method a model was made by using MATLAB software and optimized by GA. Error declined by 46.7 %. Then elevation-area-capacity curves for following years were predicted by best coefficients.
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