GEP prediction of scour around a side weir in curved channel

Side-weirs have been widely used in hydraulic and environmental engineering applications. Side-weir is known as a lateral intake structure, which are significant parts of the distribution channel in irrigation, land drainage, and urban sewerage system, by flow diversion device. Local scour involves the removal of material around piers, abutments, side-weir, spurs, and embankments. Clearwater scour depth based on five dimensional parameters: approach flow velocity (V1/Vc), water head ratio (h1–p)/h1, side-weir length (L/r), side-weir crest height (b/p) and angle of bend θ. The aim of this study is to develop a new formulation for prediction of clear-water scour of side-weir intersection along curved channel using Gene Expression Programming (GEP) which is an algorithm based on genetic algorithms (GA) and genetic programming (GP). In addition, the explicit formulations of the developed GEP models are presented. Also equations are obtained using multiple linear regressions (MLR) and multiple nonlinear regressions (MNRL). The performance of GEP is found more influential than multiple linear regression equation for predicting the clearwater scour depth at side-weir intersection along curved channel. Multiple nonlinear regression equation was quite close to GEP, which serve much simpler model with explicit formulation. First published online: 17 Mar 201

A comparison of artificial intelligence approaches in predicting discharge coefficient of streamlined weirs

In the present research, three different data-driven models (DDMs) are developed to predict the discharge coefficient of streamlined weirs (C-dstw). Some machine-learning methods (MLMs) and intelligent optimization models (IOMs) such as Random Forest (RF), Adaptive NeuroFuzzy Inference System (ANFIS), and gene expression program (GEP) methods are employed for the prediction of C-dstw. To identify input variables for the prediction of C-dstw by these DMMs, among potential parameters on C-dstw, the most effective ones including geometric features of streamlined weirs, relative eccentricity (lambda), downstream slope angle (beta), and water head over the crest of the weir (h(1)) are determined by applying Buckingham pi-theorem and cosine amplitude analyses. In this modeling, by changing architectures and fundamental parameters of the aforesaid approaches, many scenarios are defined to obtain ideal estimation results. According to statistical metrics and scatter plot, the GEP model is determined as a superior method to estimate C-dstw with high performance and accuracy. It yields an R-2 of 0.97, a Total Grade (TG) of 20, RMSE of 0.032, and MAE of 0.024. Besides, the generated mathematical equation for C-dstw in the best scenario by GEP is likened to the corresponding measured ones and the differences are within 0-10%

Regression-Based Models for Predicting Discharge Coefficient of Triangular Side Orifice

- This study introduced another technique to predict the discharge coefficient (Cd) of the triangular side orifice (TSO). This technique is based on the SPSS software as multiple linear regression (MLR) and multiple nonlinear regression (MNLR) models. These models were established using 570 experimental datasets, 70 and 30% for calibration and testing stages, respectively. These sets considered five non-dimensional parameters, including (orifice crest height, orifice length, orifice height, upstream flow depth, and Froude number of the main channel). Results showed that the MLR and MNLR models in the calibrating stage had higher determination coefficients and lower errors. In addition, the importance of the input parameters was investigated, showing that the orifice crest height and Froude number highly affect the discharge coefficient value by 36%. In the testing stage, the estimated discharge coefficient by the MLR and MNLR models stayed within the range ±12 and ‡5%, respectively, of the experimental values. The MNLR model demonstrated a high level of equivalence compared to previous studies, which provided a mathematical expression to easily predict the TSO\u27s discharge coefficient

A Performance Comparison of Machine Learning Algorithms for Arced Labyrinth Spillways

Labyrinth weirs provide an economic option for flow control structures in a variety of applications, including as spillways at dams. The cycles of labyrinth weirs are typically placed in a linear configuration. However, numerous projects place labyrinth cycles along an arc to take advantage of reservoir conditions and dam alignment, and to reduce construction costs such as narrowing the spillway chute. Practitioners must optimize more than 10 geometric variables when developing a head–discharge relationship. This is typically done using the following tools: empirical relationships, numerical modeling, and physical modeling. This study applied a new tool, machine learning, to the analysis of the geometrically complex arced labyrinth weirs. In this work, both neural networks (NN) and random forests (RF) were employed to estimate the discharge coefficient for this specific type of weir with the results of physical modeling experiments used for training. Machine learning results are critiqued in terms of accuracy, robustness, interpolation, applicability, and new insights into the hydraulic performance of arced labyrinth weirs. Results demonstrate that NN and RF algorithms can be used as a unique expression for curve fitting, although neural networks outperformed random forest when interpolating among the tested geometries

Investigation Of Hydraulic Performance On Unsymmetrical Smooth Spillway

The smooth spillway is a classic design used to spill large volume of water from the dam reservoir. Several studies of hydraulic characteristics of the spillway have been conducted experimentally and numerically in recent decades to provide a better insight into flow behaviour and predict any critical area that could endanger the structure and environment. An experimental study was carried out on a laboratory scale model to investigate basic hydraulic characteristics of the unsymmetrical smooth spillway with three sizes of discharge namely as Q1, Q2 and Q3. Next, the simulation verification were done using grid independent test (GIT), time step size analysis, and simulation time observation. In simulation, Reynolds-averaged Navier-Stokes (RANS) equation, volume of fluid (VOF) scheme and Standard κ-ω (SKW) turbulence model with appropriate boundary conditions were used to simulate the unsymmetrical smooth spillway geometry with four sizes of discharge namely as Q1, Q2, Q3 and Q4. The simulation results provide a good prediction of the hydraulic characteristics of water flow on the unsymmetrical smooth spillway and show a good agreement in terms of water surface and velocity profiles patterns with the experimental results. In the last objective, four chute piers modification models (diverge -45 degree, diverge -15 degree, converge 15 degree and converge 45 degree) were simulated using Q3 and Q4 discharge water flows and the hydraulic jump performance of all models were compared with the normal geometry. For all modification models, the converge 15 degree model showed 5.06% and 2.49% higher energy dissipation than the normal geometry model using Q3 and Q4, respectively. The chute piers modifications on unsymmetrical spillway will affect the energy dissipation performance at stilling basin and provide good insight which can be considered for spillway design in the future

A study of five types of ANN-based approaches to predict discharge coefficient of combined weir-gate

Since many years ago, flow measurement has become a fundamental issue in hydraulic engineering. One of the conventional methods of flow measurement is the use of combined structures. In this regard, using a combined structure, including a gate and a weir, is one of the approaches that has attracted the attention of researchers in this field. Therefore, in this research, five different methods based on artificial neural networks were used to predict the discharge coefficient. The networks architecture includes an input layer with four neurons, a hidden layer with seven neurons, and an output layer with one neuron. be mentioned that the number of neurons within the hidden layer is set to 4 only for the recurrent network. For the hidden layer, the logarithmic sigmoid activation function was used. Also, the linear activation function was used for the output layer. Finally, the results showed that the Levenberg-Marquardt (LM) algorithm performs better than the other methods. The convergence speed of this algorithm, which also uses the second derivative, is much higher than others. In this case, the coefficient of determination (R^2) for the training and the test stage was equal to 0.92616 and 0.94079, respectively. In addition to, the first type of rough model with the gradient descent training algorithm also had an acceptable performance and was placed in second place. Also, the sensitivity analysis on the dimensionless parameters affecting this issue showed that the H⁄d, y⁄d, b⁄B, and b⁄d parameters have maximum to minimum effect on the model results, respectively

Streamflow and sediment load prediction using linear genetic programming

Daily flow and suspended sediment discharge are two major hydrological variables that affect rivers’ morphology and ecosystem, particularly during flood events. Artificial neural networks (ANNs) have been successfully used to model and predict these variables in recent studies. However, these are implicit and cannot be simply used in practice. In this paper, linear genetic programming (LGP) approach has been suggested to develop explicit models to predict these variables in two rivers in Iran. The explicit relationships (prediction rules) evolved by LGP take the form of equations or program codes, which can be checked for its physical consistency. The results showed that the LGP outperforms ANNs to get global maximum and minimum discharges providing lowest root mean squared error and higher coefficient of efficiency both for training and validation periods.Nehirlerin morfolojisini, ekosistemi ve özellikle taşkın olaylarını etkileyen iki ana değişken askıdaki sediment ve günlük akımlardır. Yapay sinir ağları (YSA), bu değişkenleri modellemek ve tahmin etmek için yakın zamanda yapılmış çalışmalarda başarıyla kullanılmıştır. Bununla birlikte, bunlar kapalı yöntemlerdir ve pratik uygulamalarda kolaylıkla kullanılamazlar. Bu makalede, İran'daki iki nehirde bu değişkenleri tahmin etmek üzere açık modeller geliştirmek için doğrusal genetik programlama (DGP) yaklaşımı önerilmiştir. DGP tarafından geliştirilen açık ilişkiler (tahmin kuralları), fiziksel tutarlılığı açısından kontrol edilebilen denklemler veya program kodları şeklindedir. Sonuçlar, global maksimum ve minimum akımları elde etme noktasında, DGP’nin YSA’ya göre daha başarılı olduğunu gerek kalibrasyon gerekse doğrulama aşamalarında hataların karelerinin ortalamasının karekökünün en düşük, verimlilik katsayısının ise daha yüksek olmasını sağlayarak göstermiştir.No sponso

Proceedings of the International Workshop on Hydraulic Design of Low-Head Structures - IWLHS 2013

Scientific standards applicable to publication of BAWProceedings: http://izw.baw.de/publikationen/vzb_dokumente_oeffentlich/0/2020_07_BAW_Scientific_standards_conference_proceedings.pd