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Nihat Kaya - One of the best experts on this subject based on the ideXlab platform.

  • estimating Discharge Coefficient of semi elliptical side weir using anfis
    Journal of Hydrology, 2012
    Co-Authors: Faruk O Dursun, Nihat Kaya, Mahmut Firat
    Abstract:

    Summary A labyrinth weir is defined as a weir crest that is not straight in planform. The increased sill length provided by the semi-elliptical labyrinth side weirs effectively reduces upstream head to the particular Discharge. They can therefore be used to particular advantage where the width of a channel is restricted and a weir is required to pass a range of Discharges with a limited variation in upstream water level. In this study, the Discharge capacity of semi-elliptical side weirs is estimated by using Adaptive-Neuro Fuzzy Inference System (ANFIS). 675 Laboratory test results are used for determining Discharge Coefficient of semi-elliptical labyrinth side weirs. The performance of the ANFIS model is compared Multiple Linear Regression (MLR) and Nonlinear Regression (NLR) models based on performance evaluation parameters. Comparison results indicated that the ANFIS technique could be successfully employed in modeling Discharge Coefficient.

  • Discharge Coefficient of side weirs in curved channels
    Proceedings of the Institution of Civil Engineers - Water Management, 2012
    Co-Authors: Hayrullah Agaccioglu, Emin M Emiroglu, Nihat Kaya
    Abstract:

    Side weirs are flow diversion devices commonly used in irrigation, land drainage and urban sewage systems. It is essential for hydraulic and environmental engineers involved in the design of side weirs to predict the Discharge Coefficient correctly. The aim of this study is to present an accurate equation for Discharge Coefficients along the bend of sharp-crested rectangular side weirs based on a total of 1504 experimental runs. The Discharge Coefficient of the rectangular side weir along the bend depends on the dimensionless parameters of Froude number in the main channel F1, the angle of bend curvature α, the ratio of weir length to main channel width L/b, the ratio of weir length to radius of main channel centreline L/rc and the ratio of weir height to upstream flow depth p/h1. In particular, the variables L/rc and p/h1 are not seen in any equation for a rectangular side weir located on a curved channel. However, it was found that these dimensionless parameters were significantly important for the over...

  • Discharge Coefficient of a semi elliptical side weir in subcritical flow
    Flow Measurement and Instrumentation, 2011
    Co-Authors: Nihat Kaya, Emin M Emiroglu, Hayrullah Agaccioglu
    Abstract:

    Abstract A labyrinth weir is an overflow weir, folded in plan view to provide a longer total effective length for a given overall weir width. The total length of the labyrinth weir is typically three to five times the weir width. In this study, a semi-elliptical labyrinth weir was used as a side weir structure. Rectangular side weirs have attracted considerable research interest. The same, however, is not true for labyrinth side weirs. The present study investigated the hydraulic effects of semi-elliptical side weirs in order to increase their Discharge capacity. To estimate the outflow over a semi-elliptical side weir, the Discharge Coefficient in the side weir equation needs to be determined. A comprehensive laboratory study including 677 tests was conducted to determine the Discharge Coefficient of the semi-elliptical side weir. The results were analyzed to find the influence of the dimensionless weir length L / B , the dimensionless effective length L / l , the dimensionless weir height p / h 1 , the dimensionless ellipse radius b / a , and upstream Froude number F 1 on the Discharge Coefficient. It was found that the Discharge Coefficient of semi-elliptical side weirs is higher than that of classical side weirs. Additionally, a reliable equation for calculating the Discharge Coefficient of semi-elliptical side weirs is presented.

  • Discharge Coefficient for trapezoidal labyrinth side weir in subcritical flow
    Water Resources Management, 2011
    Co-Authors: Muhammet Emin Emiroglu, Nihat Kaya
    Abstract:

    The Discharge Coefficient of a trapezoidal labyrinth side weir is a function of the Froude number F 1 , the dimensionless effective crest length L/l, the dimensionless weir length L/B, the dimensionless weir height p/h 1 , and the sidewall angle α. A labyrinth weir is an overflow weir, folded in plan view to provide a longer total effective length for a given overall weir width. These weirs have advantages compared to the straight overflow weir and the standard ogee crest. Previous studies on the subject have generally focused on rectangular side weirs located on a straight channel. The present study investigates the hydraulic behavior of a trapezoidal labyrinth side weir. The results show that the Discharge Coefficient of labyrinth side weirs gives a significantly higher Coefficient value compare to that of conventional straight side weirs. Discharge Coefficient of the trapezoidal labyrinth side weir is 1.5 to 5.0 times higher than the conventional straight side weir. Consequently, an equation for the Coefficient of Discharge is introduced. The results predicted by the equation were shown to be very satisfactory using root mean square error (RMSE), mean absolute error (MAE) and correlation Coefficient (R) statistics. Copyright Springer Science+Business Media B.V. 2011

  • Discharge Coefficient for trapezoidal labyrinth side weir in subcritical flow
    Water Resources Management, 2011
    Co-Authors: Muhammet Emin Emiroglu, Nihat Kaya
    Abstract:

    The Discharge Coefficient of a trapezoidal labyrinth side weir is a function of the Froude number F1, the dimensionless effective crest length L/l, the dimensionless weir length L/B, the dimensionless weir height p/h1, and the sidewall angle α. A labyrinth weir is an overflow weir, folded in plan view to provide a longer total effective length for a given overall weir width. These weirs have advantages compared to the straight overflow weir and the standard ogee crest. Previous studies on the subject have generally focused on rectangular side weirs located on a straight channel. The present study investigates the hydraulic behavior of a trapezoidal labyrinth side weir. The results show that the Discharge Coefficient of labyrinth side weirs gives a significantly higher Coefficient value compare to that of conventional straight side weirs. Discharge Coefficient of the trapezoidal labyrinth side weir is 1.5 to 5.0 times higher than the conventional straight side weir. Consequently, an equation for the Coefficient of Discharge is introduced. The results predicted by the equation were shown to be very satisfactory using root mean square error (RMSE), mean absolute error (MAE) and correlation Coefficient (R) statistics.

Abbas Parsaie - One of the best experts on this subject based on the ideXlab platform.

  • prediction of Discharge Coefficient of cylindrical weir gate using gmdh pso
    ISH Journal of Hydraulic Engineering, 2018
    Co-Authors: Abbas Parsaie, Hazi Mohammad Azamathulla, Amir Hamzeh Haghiabi
    Abstract:

    Cylindrical weir-gate consists of a weir and a gate has been proposed for flow measurement in water engineering projects. Discharge Coefficient of weir-gate is a fundamental parameter to evaluate i...

  • improving modelling of Discharge Coefficient of triangular labyrinth lateral weirs using svm gmdh and mars techniques
    Irrigation and Drainage, 2017
    Co-Authors: Abbas Parsaie, Amir Hamzeh Haghiabi
    Abstract:

    In this study the Discharge Coefficient (Cd) of labyrinth lateral weirs was modelled and predicted using artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS), support vector machine (SVM), group method of data handling (GMDH), and multivariate adaptive regression splines (MARS) techniques. To this end, a related data set was collected from the literature. Results indicated that the proposed ANN model includes two hidden layers with eight and five neurons in the first and second hidden layers, respectively. Testing transfer functions demonstrated that the radial basis function (RBF) has the best outcome. Evaluating the performance of the ANN model shows that this model with a Coefficient of determination (R2 = 0.96) and root mean square error (RMSE =0.07) in the testing stage has suitable functionality. Reviewing the ANFIS model showed that this model with one hidden layer containing seven membership functions within the Gaussian function could achieve R2 = 0.94 and RMSE =0.136 for predicting the Discharge Coefficient. During preparation of SVM, it was found that this model with RBF as the best kernel function obtained R2 = 0.96 and RMSE =0.07. The structure of the MARS and GMDH models pointed out that the Froude number, grandiose index and ratio of upstream flow depth to the height of weir are the most effective parameters for predicting the Discharge Coefficient. Minimum R2 and RMSE achieved were equal to 0.93 and 0.08, respectively, in the testing stage for the MARS and GMDH models. Copyright © 2017 John Wiley & Sons, Ltd.

  • prediction of Discharge Coefficient of triangular labyrinth weirs using adaptive neuro fuzzy inference system
    alexandria engineering journal, 2017
    Co-Authors: Amir Hamzeh Haghiabi, Abbas Parsaie, Samad Ememgholizadeh
    Abstract:

    Abstract In this paper, the Discharge Coefficient of triangular labyrinth weir was predicted using multi-layer perceptron (MLP) neural network and Adaptive Neuro Fuzzy Inference System (ANFIS). To this purpose, 223 related dataset were collected. The Gamma Test (GT) was carried out to obtain the most affective parameters on the Discharge Coefficient. The results of the GT indicated that the ratio of length of crest of weir to the main channel width Lw/Wmc, the ratio of length of one cycle to its width (Lc/Wc) and the ratio of total upstream head flow to the weir height H/P are the most important parameters. With regarding to the results of the GT, the structure of ANFIS model was designed. The results of ANFIS model with error indices including Coefficient of determination value of 0.97 and root mean square error value of 0.03 was so suitable. Comparison the results of MLP with ANFIS model showed that both models has so suitable performance however the structure of ANFIS model is more optimal.

  • predication of Discharge Coefficient of cylindrical weir gate using adaptive neuro fuzzy inference systems anfis
    Frontiers of Structural and Civil Engineering, 2017
    Co-Authors: Abbas Parsaie, Amir Hamzeh Haghiabi, Mojtaba Saneie, Hasan Torabi
    Abstract:

    Settlement of sediments behind weirs and accumulation of materials floating on water behind gates decreases the performance of these structures. Weir-gate is a combination of weir and gate structures which solves them Infirmities. Proposing a circular shape for crest of weirs to improve their performance, investigators have proposed cylindrical shape to improve the performance of weir-gate structure and call it cylindrical weir-gate. In this research, Discharge Coefficient of weir-gate was predicated using adaptive neuro fuzzy inference systems (ANFIS). To compare the performance of ANFIS with other types of soft computing techniques, multilayer perceptron neural network (MLP) was prepared as well. Results of MLP and ANFIS showed that both models have high ability for modeling and predicting Discharge Coefficient; however, ANFIS is a bit more accurate. The sensitivity analysis of MLP and ANFIS showed that Froude number of flow at upstream of weir and ratio of gate opening height to the diameter of weir are the most effective parameters on Discharge Coefficient.

  • prediction of side weir Discharge Coefficient by support vector machine technique
    Water Science & Technology: Water Supply, 2016
    Co-Authors: Hazi Mohammad Azamathulla, Amir Hamzeh Haghiabi, Abbas Parsaie
    Abstract:

    Side weirs have many possible applications in the field of hydraulic engineering. They are also considered an important structure in hydro systems. In this study, the support vector machine (SVM) technique was employed to predict the side weir Discharge Coefficient. The performance of SVM was compared with other types of soft computing techniques such as artificial neural networks (ANN) and adaptive neuro fuzzy inference systems (ANFIS). While ANN and ANFIS models provided a good prediction performance, the SVM model with a radial basis function kernel function outperforms them. The best SVM model was developed with a gamma Coefficient and epsilon of 15 and 0.3, respectively. The SVM yielded a Coefficient of determination ( R 2 ) equal to 0.96 and 0.93 for the training and testing data. Sensitivity analyses of the ANN, ANFIS and SVM models showed that the Froude number and ratio of weir length to the flow depth upstream of the weir are the most effective parameters for the prediction of the Discharge Coefficient.

Amir Hamzeh Haghiabi - One of the best experts on this subject based on the ideXlab platform.

  • prediction of Discharge Coefficient of cylindrical weir gate using gmdh pso
    ISH Journal of Hydraulic Engineering, 2018
    Co-Authors: Abbas Parsaie, Hazi Mohammad Azamathulla, Amir Hamzeh Haghiabi
    Abstract:

    Cylindrical weir-gate consists of a weir and a gate has been proposed for flow measurement in water engineering projects. Discharge Coefficient of weir-gate is a fundamental parameter to evaluate i...

  • improving modelling of Discharge Coefficient of triangular labyrinth lateral weirs using svm gmdh and mars techniques
    Irrigation and Drainage, 2017
    Co-Authors: Abbas Parsaie, Amir Hamzeh Haghiabi
    Abstract:

    In this study the Discharge Coefficient (Cd) of labyrinth lateral weirs was modelled and predicted using artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS), support vector machine (SVM), group method of data handling (GMDH), and multivariate adaptive regression splines (MARS) techniques. To this end, a related data set was collected from the literature. Results indicated that the proposed ANN model includes two hidden layers with eight and five neurons in the first and second hidden layers, respectively. Testing transfer functions demonstrated that the radial basis function (RBF) has the best outcome. Evaluating the performance of the ANN model shows that this model with a Coefficient of determination (R2 = 0.96) and root mean square error (RMSE =0.07) in the testing stage has suitable functionality. Reviewing the ANFIS model showed that this model with one hidden layer containing seven membership functions within the Gaussian function could achieve R2 = 0.94 and RMSE =0.136 for predicting the Discharge Coefficient. During preparation of SVM, it was found that this model with RBF as the best kernel function obtained R2 = 0.96 and RMSE =0.07. The structure of the MARS and GMDH models pointed out that the Froude number, grandiose index and ratio of upstream flow depth to the height of weir are the most effective parameters for predicting the Discharge Coefficient. Minimum R2 and RMSE achieved were equal to 0.93 and 0.08, respectively, in the testing stage for the MARS and GMDH models. Copyright © 2017 John Wiley & Sons, Ltd.

  • prediction of Discharge Coefficient of triangular labyrinth weirs using adaptive neuro fuzzy inference system
    alexandria engineering journal, 2017
    Co-Authors: Amir Hamzeh Haghiabi, Abbas Parsaie, Samad Ememgholizadeh
    Abstract:

    Abstract In this paper, the Discharge Coefficient of triangular labyrinth weir was predicted using multi-layer perceptron (MLP) neural network and Adaptive Neuro Fuzzy Inference System (ANFIS). To this purpose, 223 related dataset were collected. The Gamma Test (GT) was carried out to obtain the most affective parameters on the Discharge Coefficient. The results of the GT indicated that the ratio of length of crest of weir to the main channel width Lw/Wmc, the ratio of length of one cycle to its width (Lc/Wc) and the ratio of total upstream head flow to the weir height H/P are the most important parameters. With regarding to the results of the GT, the structure of ANFIS model was designed. The results of ANFIS model with error indices including Coefficient of determination value of 0.97 and root mean square error value of 0.03 was so suitable. Comparison the results of MLP with ANFIS model showed that both models has so suitable performance however the structure of ANFIS model is more optimal.

  • predication of Discharge Coefficient of cylindrical weir gate using adaptive neuro fuzzy inference systems anfis
    Frontiers of Structural and Civil Engineering, 2017
    Co-Authors: Abbas Parsaie, Amir Hamzeh Haghiabi, Mojtaba Saneie, Hasan Torabi
    Abstract:

    Settlement of sediments behind weirs and accumulation of materials floating on water behind gates decreases the performance of these structures. Weir-gate is a combination of weir and gate structures which solves them Infirmities. Proposing a circular shape for crest of weirs to improve their performance, investigators have proposed cylindrical shape to improve the performance of weir-gate structure and call it cylindrical weir-gate. In this research, Discharge Coefficient of weir-gate was predicated using adaptive neuro fuzzy inference systems (ANFIS). To compare the performance of ANFIS with other types of soft computing techniques, multilayer perceptron neural network (MLP) was prepared as well. Results of MLP and ANFIS showed that both models have high ability for modeling and predicting Discharge Coefficient; however, ANFIS is a bit more accurate. The sensitivity analysis of MLP and ANFIS showed that Froude number of flow at upstream of weir and ratio of gate opening height to the diameter of weir are the most effective parameters on Discharge Coefficient.

  • prediction of side weir Discharge Coefficient by support vector machine technique
    Water Science & Technology: Water Supply, 2016
    Co-Authors: Hazi Mohammad Azamathulla, Amir Hamzeh Haghiabi, Abbas Parsaie
    Abstract:

    Side weirs have many possible applications in the field of hydraulic engineering. They are also considered an important structure in hydro systems. In this study, the support vector machine (SVM) technique was employed to predict the side weir Discharge Coefficient. The performance of SVM was compared with other types of soft computing techniques such as artificial neural networks (ANN) and adaptive neuro fuzzy inference systems (ANFIS). While ANN and ANFIS models provided a good prediction performance, the SVM model with a radial basis function kernel function outperforms them. The best SVM model was developed with a gamma Coefficient and epsilon of 15 and 0.3, respectively. The SVM yielded a Coefficient of determination ( R 2 ) equal to 0.96 and 0.93 for the training and testing data. Sensitivity analyses of the ANN, ANFIS and SVM models showed that the Froude number and ratio of weir length to the flow depth upstream of the weir are the most effective parameters for the prediction of the Discharge Coefficient.

Muhammet Emin Emiroglu - One of the best experts on this subject based on the ideXlab platform.

  • prediction of Discharge Coefficient for trapezoidal labyrinth side weir using a neuro fuzzy approach
    Water Resources Management, 2013
    Co-Authors: Muhammet Emin Emiroglu, Ozgur Kisi
    Abstract:

    Adaptive neuro-fuzzy inference system (ANFIS) is considered for flow over trapezoidal labyrinth side weirs located on a straight channel as a substantial part of distribution channels in irrigation systems and treatment units. To estimate the outflow over a trapezoidal labyrinth side weir, the Discharge Coefficient in the side weir equation needs to be determined in according with the effective dimensionless parameters which is Froude number, the sidewall angle, the ratios of weir length to channel width, weir length to total crest length and weir height to flow depth. 670 laboratory test results are used for determining Discharge Coefficient of trapezoidal labyrinth side weirs. The performance of the ANFIS model is compared with artificial neural networks (ANN), non-linear regression (NLR) and multi-linear regression (MLR) models. The comparing criteria used for the evaluation of the models’ performances are root mean square errors (RMSE), mean absolute errors (MAE) and determination Coefficient (R2) statistics. Comparison results indicated that the ANFIS technique could be successfully employed in modeling Discharge Coefficient. It is found that the ANFIS model with RMSE of 0.090 in test period is superior in estimation of Discharge Coefficient than the nonlinear and linear regression models with RMSE of 0.124 and 0.279, respectively.

  • Discharge Coefficient for trapezoidal labyrinth side weir in subcritical flow
    Water Resources Management, 2011
    Co-Authors: Muhammet Emin Emiroglu, Nihat Kaya
    Abstract:

    The Discharge Coefficient of a trapezoidal labyrinth side weir is a function of the Froude number F 1 , the dimensionless effective crest length L/l, the dimensionless weir length L/B, the dimensionless weir height p/h 1 , and the sidewall angle α. A labyrinth weir is an overflow weir, folded in plan view to provide a longer total effective length for a given overall weir width. These weirs have advantages compared to the straight overflow weir and the standard ogee crest. Previous studies on the subject have generally focused on rectangular side weirs located on a straight channel. The present study investigates the hydraulic behavior of a trapezoidal labyrinth side weir. The results show that the Discharge Coefficient of labyrinth side weirs gives a significantly higher Coefficient value compare to that of conventional straight side weirs. Discharge Coefficient of the trapezoidal labyrinth side weir is 1.5 to 5.0 times higher than the conventional straight side weir. Consequently, an equation for the Coefficient of Discharge is introduced. The results predicted by the equation were shown to be very satisfactory using root mean square error (RMSE), mean absolute error (MAE) and correlation Coefficient (R) statistics. Copyright Springer Science+Business Media B.V. 2011

  • Discharge Coefficient for trapezoidal labyrinth side weir in subcritical flow
    Water Resources Management, 2011
    Co-Authors: Muhammet Emin Emiroglu, Nihat Kaya
    Abstract:

    The Discharge Coefficient of a trapezoidal labyrinth side weir is a function of the Froude number F1, the dimensionless effective crest length L/l, the dimensionless weir length L/B, the dimensionless weir height p/h1, and the sidewall angle α. A labyrinth weir is an overflow weir, folded in plan view to provide a longer total effective length for a given overall weir width. These weirs have advantages compared to the straight overflow weir and the standard ogee crest. Previous studies on the subject have generally focused on rectangular side weirs located on a straight channel. The present study investigates the hydraulic behavior of a trapezoidal labyrinth side weir. The results show that the Discharge Coefficient of labyrinth side weirs gives a significantly higher Coefficient value compare to that of conventional straight side weirs. Discharge Coefficient of the trapezoidal labyrinth side weir is 1.5 to 5.0 times higher than the conventional straight side weir. Consequently, an equation for the Coefficient of Discharge is introduced. The results predicted by the equation were shown to be very satisfactory using root mean square error (RMSE), mean absolute error (MAE) and correlation Coefficient (R) statistics.

Hossein Bonakdari - One of the best experts on this subject based on the ideXlab platform.

  • application of optimized artificial and radial basis neural networks by using modified genetic algorithm on Discharge Coefficient prediction of modified labyrinth side weir with two and four cycles
    Measurement, 2020
    Co-Authors: Amir Hossein Zaji, Hossein Bonakdari, Hamed Zahedi Khameneh, Saeed Reza Khodashenas
    Abstract:

    Abstract Determining the Discharge Coefficient is one of the most important processes in designing side weirs. In this study, the structure of Artificial Neural Network (ANN) and Radial Basis Neural Network (RBNN) methods are optimized by a modified Genetic Algorithm (GA). So two new hybrid methods of Genetic Algorithm Artificial neural network (GAA) and Genetic Algorithm Radial Basis neural network (GARB), were introduced and compared with each other. The modified GA was used to find the neuron number in the hidden layers of the ANN and to find the spread value and the neuron number of the RBNN method, as well. GAA and GARB were tested for predicting the Discharge Coefficient of a modified labyrinth side weir he GARB method could successfully predict the accurate Discharge Coefficient even in cases where there is a limited number of train datasets available.

  • design of radial basis function based support vector regression in predicting the Discharge Coefficient of a side weir in a trapezoidal channel
    Applied Water Science, 2019
    Co-Authors: Hamed Azimi, Hossein Bonakdari, Isa Ebtehaj
    Abstract:

    In general, trapezoidal channels are used in irrigation and drainage networks. When installing a side weir on the side wall of a trapezoidal channel, as excess water reaches the side weir plane, additional flow from the crest of the side weir is driven into the side channel. The main aim of this study is to predict the Discharge Coefficient of rectangular side weirs located on trapezoidal channels using support vector machines (SVMs). Based on the effective parameters on the Discharge Coefficient of side weirs in trapezoidal channels, six different models (SVM 1–SVM 6) are introduced. According to the analysis results of SVM 1–SVM 6 models, the superior model is introduced as a function of the Froude number (Fr), ratio of side weir length to the bottom width of a trapezoidal channel (L/b), ratio of side weir length to the flow depth upstream of the weir (L/y1), side slope of the trapezoidal channel (m) and ratio of flow depth upstream of the weir to the trapezoidal channel bottom width (y1/b). Based on the simulation results, the superior model has a reasonable accuracy. For example, the root mean square error, mean absolute relative error and correlation Coefficient (R2) values calculated for the superior training model are 0.0156, 0.0327 and 0.884, respectively. Furthermore, the ratio of side weir length to trapezoidal channel bottom width (L/b) is identified as the most effective input parameter for modeling Discharge Coefficient. Additionally, a matrix is presented for superior model to estimate Discharge Coefficient of the side weirs.

  • development of more accurate Discharge Coefficient prediction equations for rectangular side weirs using adaptive neuro fuzzy inference system and generalized group method of data handling
    Measurement, 2018
    Co-Authors: Isa Ebtehaj, Hossein Bonakdari, Bahram Gharabaghi
    Abstract:

    A rectangular side weir is a hydraulic structure commonly utilized all around the world in urban stormwater and wastewater sewer networks and in irrigation drainage systems to deviate excessive flow passing through the main channel. In this study, a genetic algorithm (GA) is employed to identify the best selection of adaptive neuro-fuzzy inference system (ANFIS) membership functions and the evolutionary design of a generalized group method of data handling (GMDH) structure for prediction of the side weir Discharge Coefficient. Moreover, the Singular Value Decomposition (SVD) method is applied to calculate the linear parameters of the ANFIS results and linear Coefficient vectors in GMDH (ANFIS-GA/SVD and GMDH-GA/SVD). The side weir dimensionless length, Froude number, the ratio of weir height to upstream flow depth, and the ratio of weir length to upstream flow depth serve as inputs to the ANFIS-GA/SVD and GMDH-GA/SVD models to forecast the Discharge Coefficient. We compared the results of these multi-objective methods with the single-objective methods and found that the multi-objective methods are superior regarding accuracy. Sensitivity analysis is also carried out to determine the impact of each parameter on Discharge Coefficient estimation. ANFIS-GA/SVD outperformed ANFIS-GA, GMDH-GA/SVD, GMDH-GA and existing regression-based and machine learning-based equations. The uncertainty analysis is also carried out to assess the quantitative performance of all models. The results indicate that the uncertainty width for the best model (ANFIS-GA/SVD) is ±0.0067.

  • Optimum Support Vector Regression for Discharge Coefficient of Modified Side Weirs Prediction
    INAE Letters, 2017
    Co-Authors: Amir Hossein Zaji, Hossein Bonakdari
    Abstract:

    Designing procedure of economical and safe side weirs that are used in various hydraulic structures such as intakes and deviation systems, essentially needs the ability to predict the side weirs’ Discharge capacity accurately. In this paper, the Discharge Coefficient of a modified labyrinth side weir was modeled by employing the support vector regression (SVR) method. To find the optimum SVR scenario, eight different kernel functions and six different input combinations were investigated. The accuracy of the SVR models were compared with two nonlinear regression equations from other published studies. The results showed that the SVR model with Polynomial Kernel Function and $$w/L,Fr_{1} /\sin \theta^{\prime},w/Y_{1}$$ w / L , F r 1 / sin θ ′ , w / Y 1 and $$w\sin \theta^{\prime}/Y_{1}$$ w sin θ ′ / Y 1 as input parameters performs better than other models in predicting the Discharge Coefficient. Where w , L , θ′ , and Y _ 1 are the height of crest, weir length, oblique side-weir included angle, and upstream flow depth, respectively. Also, the results showed that the SVR model with mean square error ( RMSE ) of 0.050 performs much better than the two other nonlinear regression published equations with RMSE of 0.121 and 0.4270, respectively.

  • adaptive neuro fuzzy inference system multi objective optimization using the genetic algorithm singular value decomposition method for modelling the Discharge Coefficient in rectangular sharp crested side weirs
    Engineering Optimization, 2016
    Co-Authors: Fatemeh Khoshbin, Amir Hossein Zaji, Hossein Bonakdari, Isa Ebtehaj, Seyed Hamed Ashraf Talesh, Hamed Azimi
    Abstract:

    In the present article, the adaptive neuro-fuzzy inference system (ANFIS) is employed to model the Discharge Coefficient in rectangular sharp-crested side weirs. The genetic algorithm (GA) is used for the optimum selection of membership functions, while the singular value decomposition (SVD) method helps in computing the linear parameters of the ANFIS results section (GA/SVD-ANFIS). The effect of each dimensionless parameter on Discharge Coefficient prediction is examined in five different models to conduct sensitivity analysis by applying the above-mentioned dimensionless parameters. Two different sets of experimental data are utilized to examine the models and obtain the best model. The study results indicate that the model designed through GA/SVD-ANFIS predicts the Discharge Coefficient with a good level of accuracy (mean absolute percentage error = 3.362 and root mean square error = 0.027). Moreover, comparing this method with existing equations and the multi-layer perceptron–artificial neural network...