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The aim of this study is to numerically investigate the effect of bed slope on the hydraulic performance of side weirs in supercritical and subcritical flow regimes. Increasing the bed slope decreases the weir efficiency and discharge coefficient in the subcritical flow regime up to 14.54% and in the supercritical flow regime up to 9.26%. By examining the longitudinal velocity distribution, it was observed that in the subcritical flow regime, the maximum longitudinal velocity occurs at the beginning of the upstream of the side weir, and by moving towards the downstream of the weir, the flow velocity decreases and increases again after leaving the length of the weir. In the supercritical flow regime, it has an increasing trend when the flow enters from the upstream of the side weir. The increase in discharge coefficient with the increase of the weir height varied between 4.6% and 7.8%. With increase of the slope of the channel bed, the velocity along the length of the weir increases by 10.88% and 6.17%, respectively, in the subcritical and supercritical regimes, and the transverse velocity along the transverse direction for the subcritical and supercritical flow regimes decreased by 22.23% and 4.80%, respectively. [ABSTRACT FROM AUTHOR]

Background and Objectives: The present research was carried out with the aim of investigating and determining sediment changes in the Karun River, in the Zergan-Farsyat region, taking into account the existing conditions and the time of formation of the ring. The simulation of the river at a distance of about 75 km in Zargan-Farsyat region was done using HEC-RAS model. One of the main and most important issues in the water sector is the supply of required water and its transfer. One of the factors that reduce the transfer efficiency is sedimentation in water transfer channels and irrigation facilities. Knowledge of sediments and their control is essential for water engineering specialists. Therefore, in this study, the pattern of flow and transport of sediments in the network of open channels has been examined. Also, in this study, in order to determine the changes in the flow and sedimentation pattern of the Karun River in the existing conditions and when the loop was created. Gibson et al. (2010) modeled the COWLITZ River with respect to the moving bed and used the HEC-RAS model for simulation. They did the modeling for the last 20 miles of the river because flooding was more likely due to sedimentation in this area. The purpose of this study was to investigate the effects of sedimentation on the increase of flood risk in the long term. The Laursen- Copeland relation was used in the sediment calculations because the sediments ranged from very fine sand to large rubble. They also stated that the ability to add bed particle size in 20 different classes has been added to the HEC-RAS model in version 4.1. Materials and Methods: Karun River is one of the largest and longest rivers in Iran and collects the waters of vast areas of the country and finally leads to the Persian Gulf. The length of the Karun river is about 890 kilometers and the area of its watershed is 62570 square kilometers. The geographic location of this research is based on the UTM geographic system, around 32°22'33'' to 17°45'32'' north latitude and 48°22'40'' to 19°57'48'' east longitude. According to Figure 1, the total length of the studied route is about 75 km between Zargan and Farsiat and includes 155 cross sections, and the approximate length of the meander is 13.4 km. In order to simulate the hydraulic flow and sedimentation of the river by HEC-RAS model, geometric information (cross sections, distance of sections from the downstream control section, Manning's roughness coefficient and opening and narrowing coefficients), hydraulic information (flow information and water level level at the lower station) meteorological (water temperature) and sedimentary (related to flow-sediment discharge and bed sediments granularity) is necessary. To calculate the sediment transport rate, various relationships are predicted in the model (Young, Eckers-White, England-Hansen, Larsen (Copeland), Meer-Peter and Muller, Tofalti and Wilcock) so that the user can find the appropriate relationship for the studied surface. They choose. Gibson et al. (2010) stated that the ability to add bed particle size in 20 different classes was added to the HEC-RAS model in version 4.1. Results: Generally, it can be said that after the formation of the ring, the upstream distance (R1) of its sedimentation decreases from upstream to downstream until it undergoes erosion in the fork of the river and the middle distance (R2) changes from an erodible state to a sedimentary state, and the middle distance (R2) changes from the erodible state to the sedimentary state, and the distance downstream of the ring (R3) remains almost unchanged and shows very little tendency to erode. In section 21, if there is no ring, this section tends to erosion, and when the ring is created, it tends to sedimentation, which can be seen that at the end of the hydrograph, in the absence of a ring, there is approximately 1 cm of erosion on the bottom of the river. It has happened and in the case of the presence of a ring, 11 cm of sedimentation has taken place, and the amount of sedimentation reaches 23 cm almost in the middle of the hydrograph. Conclusion: The middle part of the Karun River (R2) turns from erodible to sediment by creating a ring, which is the reason for the river's tendency to deposit sediment in this part after creating the ring. Also, the decrease in flow rate and as a result the decrease in flow speed in this section is due to the division of the flow of the two branches of the river upstream of this section. In general, it can be said that after the formation of the upstream interval ring (R1), its sediment decreases from upstream to downstream until it undergoes erosion in the fork of the river, and the middle interval (R2) changes from erodible to sedimentary, and the interval of the base ring (R3) remains almost unchanged and has very little tendency to erode. [ABSTRACT FROM AUTHOR]

The present research investigates K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) algorithms for evaluating hydraulic parameters of inclined drops equipped with fishway elements. 395 measurements were analyzed using these two algorithms with a focus on E/E and yd/H. To evaluate algorithm effectiveness, three accuracy measures: R2, RMSE, and the KGE are used for a number of different scenarios. The SVM results show that in the first and second scenarios, respectively, model number 13 yielded RMSE=0.0156, R2=0.973, and KGE=0.961 for the training mode and RMSE=0.0241, R2=0.962, and KGE=0.952 for the testing mode. The KNN algorithm with 75% of the total data employed for the initial sample size gave the best results with RMSE=0.0121, R2=0.986, and KGE=0.975 for the first scenario and RMSE= 0.0123, R2= 0.986, and KGE= 0.975 for the second scenario. Also, the results stated that the RBF kernel function is superior among the kernel functions of the SVM algorithm and the Cityblock distance measurement method among other methods of the K-NN algorithm. On the other hand, the K-NN method was superior to the SVM method. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the effects of uncertainty in soil characteristics and dam geometry on seepage flow using the hybrid Multivariate Adaptive Regression Splines (MARS) and Monte Carlo Method (MCM). A computer program based on Darcy flow is developed in the Fortran language to calculate the discharge flow. After validating the numerical FORTRAN code with experimental outputs, firstly, the Deterministic Finite Element Method (DFEM) was used to obtain Seepage Exit Discharge (SED) in Steady State Condition (SSC), and MCM was used for probabilistic analysis to account for uncertainty in random parameters. The program monitored Pore Water Pressure (PWP) changes and integrated them into the time/space domains. To ensure minimal error, the results of the models were compared by Standard Error Calculation (SEC). The research also introduced a new component to compare the seepage flow resulting from the analysis of models in a dimensionless manner called the Effective Discharge MARSplines (EDM). In the present research, the combination of Machine Learning (ML) and MCM algorithms was used in an innovative way for Random Finite Element Method (RFEM) calculations. The results of the research indicate that a 17.9% increase in the Hd/Hu ratio in the deterministic analysis results in a 29.3% decrease in EDM, while in the probabilistic analysis, a similar increase leads to a 19.02% decrease in EDM. Upon comparing deterministic and stochastic models, it can be concluded that deterministic analysis is more accurate and exhibits less error when compared to the probabilistic model. [ABSTRACT FROM AUTHOR]

In the present study, an artificial neural network and its combination with wavelet theory are used as the computational tool to predict the depth of local scouring from the bridge pier. The five variables measured are the pier diameter of the bridge, the critical and the average velocities, the average diameter of the bed aggregates, and the flow depth. In this study, the neural wavelet method is used as a preprocessor. The data was passed through the wavelet filter and then passed to the artificial neural network. Among the various wavelet functions used for preprocessing, the dmey function results in the highest correlation coefficient and the lowest RMSE and is more efficient than other functions. In the wavelet-neural network compilation method, the neural network activator function was replaced by different wavelet functions. The results show that the neural network method with the Polywog4 wavelet activator function with a correlation coefficient of 87% is an improvement of 8.75% compared to the normal neural network model. By performing data filtering by wavelet and using the resulting coefficients in the neural network, the resulting correlation coefficient is 82%, only a 2.5% improvement compared to the normal neural network. By analyzing the results obtained from neural network methods, the wavelet-neural network predicted errors compared to experimental observations were 8.26, 1.56, and 1.24%, respectively. According to the evaluation criteria, combination of the best effective hydraulic parameters, the combination of wavelet function and neural network and the number of neural network neurons achieved the best results. [ABSTRACT FROM AUTHOR]

The basic equation of gradually varied flow (GVF) describes gradual changes in its water surface elevation. One of the challenges of numerical integration is determining the appropriate integration spatial step size. In this study, the water surface elevations in GVF are calculated by adaptive Runge-Kutta method and adaptive Newton-Raphson method. The steps are calculated using error estimation during calculation, this procedure is smart and increases accuracy and speed of computation of the water surface profiles in GVF. In order to show the efficiency and accuracy of these methods, the results of these two methods are compared with each other and the results obtained through direct Step method in several applications. The differences between the results are negligibly small. Finally, as practical applications the proposed methods are applied to study changes of water surface profile due to channel bed aggradation in GVF and cyclic looped network. Obtained results show 56% reduction in the CPU time compared with previous method (Simultaneous solution method). [ABSTRACT FROM AUTHOR]