Rasoul Daneshfaraz; Ehsan Aminvash; Reza Mirzaee; John Abraham
Abstract
In this research, the performance of support vector machine in predicting relativeenergy dissipation in non-prismatic channel and rough bed with trapezoidalelements has been investigated. To achieve the objectives of the present study,136 series of laboratory data are analyzed under the same laboratory ...
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In this research, the performance of support vector machine in predicting relativeenergy dissipation in non-prismatic channel and rough bed with trapezoidalelements has been investigated. To achieve the objectives of the present study,136 series of laboratory data are analyzed under the same laboratory conditionsusing a support vector machine. The present study entered the support vectormachine network without dimension in two different scenarios with a height of 1.50and 3.0 cm rough elements. Two statistical criteria of Root Mean Square Error andcoefficient of determination are used to evaluate the efficiency of input compounds.Hydraulically, the results show that at both heights of the rough elements, energydissipation increased with increasing Froude number. The results of the supportvector machine show that the height of the roughness element is 1.50 cm in thefirst scenario, combination number 6 with R2 = 0.990 and RMSE = 0.0129 fortraining mode and R2 = 0.993 and RMSE = 0.032 for testing mode and the heightof the roughness element 3.0 in the second scenario, combination number 6 withR2 = 0.989 and RMSE = 0.0112 for training mode, R2 = 0.994 and RMSE = 0.0224for testing mode are select as the best models. Finally, sensitivity analysis isperformed on the parameters and H / y1 parameter is selected as the most effectiveparameter.
Mahdi Majedi-Asl; Rasoul Daneshfaraz; Mehdi Fuladipanah; John Abraham; Mohammad Bagherzadeh
Abstract
In this paper, two groups of datasets including Froehlich (1988) and USGS were implemented to simulate scour depth for bridge piers of three shapes (circular, sharp-nose and rectangular) using support vector machine (SVM) algorithm. The results of the SVM were compared with gene expression programming ...
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In this paper, two groups of datasets including Froehlich (1988) and USGS were implemented to simulate scour depth for bridge piers of three shapes (circular, sharp-nose and rectangular) using support vector machine (SVM) algorithm. The results of the SVM were compared with gene expression programming (GEP) and the non-linear regression model. Independent parameters extracted using dimensional analysis were Froud number (Fr), the ratio of pier length to pier width (L/b), the ratio of sediment particle diameters (d50/d84), the ratio of sediment mean size to pier width (d50/b) and attack angle of water flow (α). Different combinations of independent variables were used to achieve optimum performance of the simulator. The results showed that among three simulators, the SVM algorithm had the best performance to predict local scour depth. The sensitivity analysis revealed that among independent parameters, the descending order of effectivity was Fr, sediment size, L/b, and α.
