Evaluation of ANN, GEP, and Regression Models
to Estimate the Discharge Coefficient
for the Rectangular Broad-Crested Weir

Safari, Samira; Takarli, Atefeh; Salarian, Mohammad; Banejad, Hossein; Heydari, Mohammad Hossein; Ghadim, Hamed Benisi

doi:10.15244/pjoes/147592

Cited by 2 publications

(2 citation statements)

References 34 publications

(45 reference statements)

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“…Emami et al 26 used the Walnut algorithm and SVR method to predict the C d of triangular labyrinth weirs. Safari et al 27 evaluated ANN, GEP, and regression models to estimate the C d of the broad-crested weir. The results showed that ANN estimates the C d better than GEP models and regression models.…”

Section: Introductionmentioning

confidence: 99%

LXGB: a machine learning algorithm for estimating the discharge coefficient of pseudo-cosine labyrinth weir

Emami

Parsa

2023

Sci Rep

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One of the practical and financial solutions to increase the efficiency of weirs is to modify the geometry of the plan and increase the length of the weir to a specific width. This increases the discharge coefficient (Cd) of the weir. In this study, a new weir referred to pseudo-cosine labyrinth weir (PCLW) was introduced. A hybrid machine learning LXGB algorithm was introduced to estimate the Cd of the PCLW. The LXGB is a combination of the linear population size reduction history-based adaptive differential evolution (LSHADE) and extreme gradient boosting (XGB) algorithm. Seven different input scenarios were presented to estimate the discharge coefficient of the PCLW weir. To train and test the proposed method, 132 data series, including geometric and hydraulic parameters from PCLW1 and PCLW2 models were used. The root mean square error (RMSE), relative root mean square error (RRMSE), and Nash–Sutcliffe model efficiency coefficient (NSE) indices were used to evaluate the proposed approach. The results showed that the input variables were the ratio of the radius to the weir height (R/W), the ratio of the length of the weir to the weir height (L/W), and the ratio of the hydraulic head to the weir height (H/W), with the average values of RMSE = 0.009, RRMSE = 0.010, and NSE = 0.977 provided better results in estimating the Cd of PCLW1 and PCLW2 models. The improvement compared to SAELM, ANFIS-FFA, GEP, and ANN in terms of R2 is 2.06%, 3.09%, 1.03%, and 5.15%. In general, intelligent hybrid approaches can be introduced as the most suitable method for estimating the Cd of PCLW weirs.

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Section: Introductionmentioning

confidence: 99%

LXGB: a machine learning algorithm for estimating the discharge coefficient of pseudo-cosine labyrinth weir

Emami

Parsa

2023

Sci Rep

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“…Scientists have examined the utilization of artificial intelligence methods, such as artificial neural networks, adaptive neuro fuzzy inference systems, support vector machines, and group technique of data processing [26,37,42]. Safari et al [43] evaluated various models -Artificial Neural Networks (ANN), Gene Expression Programming (GEP), and regression models -in estimating the discharge coefficient for rectangular broadcrested weirs. The main finding seems to be a comparative analysis of these models in accurately predicting the flow over a specific type of weir, highlighting the strengths and weaknesses of each approach.…”

Section: Introductionmentioning

confidence: 99%

Hydraulic Behavior of Weir-Gate Structure with Rectangular Side Obstacle under Free and Submerged Flow Conditions

Qasim,

Dawood

2023

I2M

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This research examines the behavior of a weir-gate hydraulic structure when subjected to the presence or absence of a side obstacle. The study focuses on two scenarios, free flow and submerged flow, and employs a side obstacle with dimensions of 15 cm in height, 30 cm in length, and 1 cm in thickness, located 30 cm downstream from the weir-gate structure. A correlation matrix is derived using measured and calculated data to establish the relationship between various variables, both dimensional and non-dimensional. The results show a clear correlation between Froude number and discharge coefficient, while a random trend is observed between discharge coefficient and Reynolds number. A proper trend is found between Reynolds number and Froude number at the downstream location. The discharge coefficient is significantly influenced by factors such as gate water depth, weir water head, and vertical distance between the weir and gate. Additionally, the study investigates the effect of average water depth at the downstream on discharge and flow velocity. Two models, linear regression and artificial neural network, are developed and tested to estimate the coefficient of discharge (Cd). The ANN model proves to be the best-fit model for all scenarios, with an MSE of 0.00013141 and an R-squared value of 0.99. The machine learning algorithms used in this study demonstrate an increase in prediction accuracy for Cd.

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Evaluation of ANN, GEP, and Regression Models to Estimate the Discharge Coefficient for the Rectangular Broad-Crested Weir

Cited by 2 publications

References 34 publications

LXGB: a machine learning algorithm for estimating the discharge coefficient of pseudo-cosine labyrinth weir

LXGB: a machine learning algorithm for estimating the discharge coefficient of pseudo-cosine labyrinth weir

Hydraulic Behavior of Weir-Gate Structure with Rectangular Side Obstacle under Free and Submerged Flow Conditions

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