JWSS - Journal of Water and Soil Science

River flow routing has been a significant issue in hydraulic engineering. The main goal of this research work was solving Saint-Venant equations by using the semi-implicit finite difference scheme and considering energy conservation principle at the discontinuous points of flow field. In this model, with the first order accuracy, the flux limiter scheme and Upwind for the scheme are used for the satisfaction of TVD condition and discretization of the advection phrase in the momentum equation, respectively. By using three assessment functions including Nash-.Sutcliffe, sum square error and correlation coefficient, the performance of the model was evaluated for flood routing through Lighvan-Chai River between Lighvan and Hervi hydrometric stations with the application of twenty cross-sections. Manning roughness coefficient as a parameter for calibration and verification processes was determined to be 0.028. Finally, a comparison was made between nonlinear Muskingham hydrological method and the presented model through the same river reach.  The amount of assessment functions for the semi-implicit model was calculated to be more than the hydrological one. The results showed that the presented model not only had high calculative efficiency and no limitation in time step calculation, but also displayed more accuracy for the hydrodynamic characteristics of flow.

A labyrinth weir is a nonlinear weir folded in the plan-view which increases the crest length and the flow rate for a given channel width and an upstream flow depth. Nowadays, a labyrinth weir is an attractive alternative for those weirs that have a problem in passing the probable maximum flood. The three-dimensional flow pattern and unlimited geometric parameters provide a major challenge to the designers of these weirs. The present study aimed at determining discharge coefficients of sharp-crested triangular labyrinth weirs using the support vector machine (SVM). The results were compared with the experimental data. For this purpose, 123 laboratory test data including  geometric and hydraulic parameters such as vertex angle (θ), magnification ratio (L/B), head water ratio (h/w), Froude number (Fr), Weber Number (We) and Reynolds number (Re) were used. The results showed that the SVM-based model produced the most accurate results when only three geometric parameters, e.g. (h/w, θ, L/B), were introduced as the input parameters (R² = 0.974, Root mean square error [RMSE] = 0.0118, mean absolute error [MAE] =0.0112 and mean normal error [MNE] =0.017 for the test stage). Also, for these weirs, polynomials linear and nonlinear regression equations were presented. Finally, the discharge coefficient of sharp-crested triangular labyrinth weirs based on the Rehbock equation was evaluated and compared with the SVM using nonlinear and linear regression methods.

The prediction of local scouring as a dynamic and nonlinear phenomenon using methods of acceptable predictive capability has always been of interest to researchers. The shape of the bridge pier is one of the important factors in the formation and magnitude of the scour hole. In this paper, the scour depth of three bridge piers with cylindrical, sharp nose and rectangular shapes was predicted in two scenarios using the support vector machine algorithm with 395 field data obtained from the US Geological Survey and Froehlich (1988), based on different combinations of dimensionless parameters as the water attack angle (α), Froud number (Fr), the ration of pier length to width (l/b), and the ratio of mean sediment size to pier width (D50/b). The results of the study, while confirming the acceptable performance of the SVM algorithm for all piers in both scenarios, showed that in the first and second scenarios, the most optimal performance was related to the rectangular pier shape with correlation coefficient of 0.8702 and 0.8838, with and maximum Ds _(DDR) values of 0.854 and 1.229 respectively, during the testing phase. The positive effect of increasing the number of data on the performance of the SVM algorithm was also confirmed by further probing the evaluation indicators. The results of the comparison pointed out the overestimation of the predicted scour depth values of absolute error between 11% to 35%.