Triangular Side Weir Discharge Coefficient Calculation and Comparison Using ANN

Hayawi, Hana A.; Al-Talib, Azza N.; Kattab, Noor I.

doi:10.1007/s41403-019-00066-w

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…The numerical analysis on inclined side weir was investigated by Ahmed (2011), Ahmed et al (2013) and Ahmed (2015). A powerful tool is recently used to solve complex nonlinear and multi-linear regression equations in hydraulic engineering such as artificial neural network (ANN), genetic programming (GP) and statistic's analysis using Monte Carlo method, Kisi et al (2012), Ahmed (2018), Hayawi et al (2019) and Ahmed and Anna (2020).…”

Section: Introductionmentioning

confidence: 99%

Gene Expression Programming (GEP) to predict coefficient of discharge for oblique side weir

Mohammed

Sharifi

2020

Appl Water Sci

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In irrigation and drainage structures, side weir is widely used for flow diversion from main to branch channels. Side weir is also used as a measuring device for discharge measurements, so discharge coefficient was mainly studied in many previous studies. Skew side weir was not taking a good highlight in previous studies and literature, so the present work discharge coefficient calculation for the skew side weir was adopted and studied. Multiple Linear Regression (MLR) and Gene Expression Programming (GEP) tools were used in the present study and compared with observed values of C d. The mean absolute error for C d observed and calculated in MLR and GEP was not exceeded 5%. The C d values for skew side weir ranged from (0.65) to (0.85), while its values for straight vertical side from previous literature weir ranged from (0.45) to (0.65); this mean skew side weir can be used for increase in discharge diversion to the branch channel at the same water levels by 27%. The Akaike information criteria (AIC) with (AICs), root-mean-square error (RMSE), mean absolute relative error (MARE) and scatter index (SI) are used in this study for measuring the GEP model performance. From results, the GEP model has AIC = − 216.51, AICs = − 918.51, RMSE = 0.004653, MARE = 0.005234, R 2 = 0.994 and SI = 0.006231 performed the best. According to previous results, the new equation presented through GEP can be adopted for discharge coefficient calculation in skew side weir.

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Section: Introductionmentioning

confidence: 99%

Gene Expression Programming (GEP) to predict coefficient of discharge for oblique side weir

Mohammed

Sharifi

2020

Appl Water Sci

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A review of studies on estimating the discharge coefficient of flow control structures based on the soft computing models

Haghbin

Sharafati

2022

Flow Measurement and Instrumentation

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Utilization of ANN technique to estimate the discharge coefficient for trapezoidal weir-gate

2022

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Developing and assessing the performance of water projects and irrigation networks is based on many factors, such as flow measurement. Weir and gate structures have been extensively utilized for flow measurement and to get rid of sediments. The process of modeling and estimating the coefficient of discharge in the weir is an essential part of hydraulic engineering. Recently the application of computer skills was adopted instead of traditional methods. In the present study, the artificial neural network (ANN) was adopted to estimate the coefficient of discharge for a combined weir that consisted of the trapezoidal weir and rectangular gate. For this purpose, the experimental data were collected and analyzed. The dimensional analysis was used to identify the effective dimensionless parameters related to the discharge coefficient. The developed ANN network structure was designed as 6-10-1 and adopted the default scaled conjugate gradient algorithm for training using SPSS V24 software. It was found that the proposed model with ten neurons was highly accurate in predicting the discharge coefficient. The sensitivity analysis was adopted to assess the performance of the ANN using different numbers of effective input parameters. Assessing five models, the ratio of upstream head to gate height (H/d), slope of trapezoidal angle (tan θ), and the ratio of distance between weir and gate and gate height (y/d) parameters are adequate for estimating the discharge coefficient compared to other parameters. ANN model with input parameters of H/d, h/d (h is the flow depth over the trapezoidal weir), b g/d (b g is the gate width), tan θ, b g/b (b is the total width of flume), and y/d shows reasonable accuracy with acceptable statistical indicators, coefficient of determination (R 2 = 0.87), relative error (RE = 0.096), and mean squared error (MSE = 1.86) for the discharge coefficient. The ANN model gave a good idea about which factors are more effective on the discharge coefficient, and the process of training the network is more accessible than the traditional method which represent the discharge coefficient by equation.

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Triangular Side Weir Discharge Coefficient Calculation and Comparison Using ANN

Cited by 3 publications

References 5 publications

Gene Expression Programming (GEP) to predict coefficient of discharge for oblique side weir

Gene Expression Programming (GEP) to predict coefficient of discharge for oblique side weir

A review of studies on estimating the discharge coefficient of flow control structures based on the soft computing models

Utilization of ANN technique to estimate the discharge coefficient for trapezoidal weir-gate

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