In the present study, the performance of the support vector machine for estimating vertical drop hydraulic parameters in the presence of dual horizontal screens has been investigated. For this purpose, 120 different laboratory data were used to estimate three parameters of the drop: the relative length, the downstream relative depth, and the residual relative energy in the support vector machine. For each parameter, 12 models were analyzed by using a support vector machine. The performance of the models was evaluated with statistical criteria (R2, DC, and RMSE) and the best model was introduced for each of the parameters. The evaluation criteria for the relative length of the vertical drop equipped with dual horizontal screens for the testing stage are R2 = 0.992, DC = 0.981 and, RMSE = 0.050. Also, the values of the downstream relative depth evaluation indicators for the testing stage are R2 = 0.9866, DC = 0.980 and, RMSE = 0.0064. For the residual relative energy parameter, the values of the residual relative energy evaluation indicators are R2 = 0.9949, DC = 0.9853 and RMSE = 0.0056. The results showed that capacity for this approach to predict the hydraulic performance of these systems with accuracy.

The present study investigated the application of support vector machine algorithms for predicting hydraulic parameters of a vertical drop equipped with horizontal screens. The study incorporated varying sizes of a rectangular channel. Horizontal screens, in addition to being able to dissipate the destructive energy of the flow, cause turbulence. The turbulence in turn supplies oxygen to the system through the promotion of air–water mixing. To achieve the objectives of the present study, 164 experiments were analyzed under the same experimental conditions using a support vector machine. The approach utilized dimensionless terms that included scenario 1: the relative energy consumption and scenario 2: the relative pool depth. The performance of the models was evaluated with statistical criteria (RMSE, R2 and KGE) and the best model was introduced for each of the parameters. RMSE is the root mean square error, R2 is the correlation coefficient and KGE is the Kling–Gupta criterion. The results of the support vector machine showed that for the first scenario, the third combination with R2 = 0.991, RMSE = 0.00565 and KGE = 0.998 for the training mode and R2 = 0.991, RMSE = 0.00489 and KGE = 0.991 for the testing mode were optimal. For the second scenario, the third combination with R2 = 0.988, RMSE = 0.0395 and KGE = 0.998 for the training mode and R2 = 0.988, RMSE = 0.0389 and KGE = 0.993 for the testing mode were selected. Finally, a sensitivity analysis was performed that showed that the yc/H and D/H parameters are the most effective parameters for predicting relative energy dissipation and relative pool depth, respectively.

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This study aims to provide a way to increase the energy dissipation of flow in the inclined drop with environmental and economic considerations. Eighty-one experiments were performed on three types of simple inclined drop and inclined drop equipped with hole and without hole fishway elements with a 200~600 L/min flow rate. In this study, the effect of using fishway elements on hydraulic parameters regarding flow pattern, energy dissipation, relative downstream depth, relative aeration length, relative length of the hydraulic jump, and downstream Froude number of an inclined drop was investigated through physical modeling following the symmetry law. The results showed that in all experimental models, with increasing the relative critical depth parameter, the energy dissipation values increase, and the downstream Froude number decreases. The parameters of relative downstream depth, relative length of a hydraulic jump, and relative aeration length also increase with increasing relative critical depth. On average, 88% of the flow energy dissipation increases with the design of the fishway elements on the structure compared to the simple drop. Model M7 (with holes fish elements) shows the highest energy dissipation, and Model M2 (without holes fish elements) has the highest flow aeration length and relative downstream water depth.

This study investigates the characteristics of free and submerged hydraulic jumps on the triangular bed roughness in various T/I ratios (i.e., height and distance of roughness) using CFD modeling techniques. The accuracy of numerical modeling outcomes was checked and compared using artificial intelligence methods, namely Support Vector Machines (SVM), Gene Expression Programming (GEP), and Random Forest (RF). The results of the FLOW-3D® model and experimental data showed that the overall mean value of relative error is 4.1%, which confirms the numerical model’s ability to predict the characteristics of the free and submerged jumps. The SVM model with a minimum of Root Mean Square Error (RMSE) and a maximum of correlation coefficient (R2), compared with GEP and RF models in the training and testing phases for predicting the sequent depth ratio (y2/y1), submerged depth ratio (y3/y1), tailwater depth ratio (y4/y1), length ratio of jumps (Lj/y2*) and energy dissipation (ΔE/E1), was recognized as the best model. Moreover, the best result for predicting the length ratio of free jumps (Ljf/y2*) in the optimal gamma is γ = 10 and the length ratio of submerged jumps (Ljs/y2*) is γ = 0.60. Based on sensitivity analysis, the Froude number has the greatest effect on predicting the (y3/y1) compared with submergence factors (SF) and T/I. By omitting this parameter, the prediction accuracy is significantly reduced. Finally, the relationships with good correlation coefficients for the mentioned parameters in free and submerged jumps were presented based on numerical results.

In the present study, FLOW-3D software was used to simulate energy dissipation by a serrated-edge drop, downstream of this structure. For this purpose, 2, 3, and 4 serrations with two series of relative dimensions at the edge of the vertical drop, with a relative critical depth range of 0.2–0.35 were used for simulation. Then, using Artificial Neural Network (ANN), Support Vector Machine (SVM), and Gene Expression Program (GEP) methods, the accuracy of numerical models was evaluated. Results showed that increasing dimensions of the edges increased energy dissipation, and the highest and lowest energy dissipation was related to the models with 3 and 4 serrations, respectively, Compared to the edgeless state, the 4-edge model, with relative dimension of 0.1, increased energy dissipation by an average of 20%, and the 3-edge model, with relative dimension of 0.15, by an average of 69%. Results of energy dissipation prediction using ANN, SVM and GEP methods showed that although all three models have good accuracy for estimating energy dissipation, the accuracy of ANN method with RMSE of 0.0081 and R2 of 0.9938 in training phase and RMSE of 0.0125 and R2 of 0.9805 in testing phase, is higher than the other two methods.

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.