Simulation of discharge coefficient of side weirs placed on convergent canals using modern self-adaptive extreme learning machine

Gharib, Reza; Heydari, Majeid; Kardar, Saeid; Shabanlou, Saeid

doi:10.1007/s13201-019-1136-0

Cited by 17 publications

(3 citation statements)

References 26 publications

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“…The aforementioned studies have demonstrated the effectiveness of these approaches in precisely determining the discharge coefficients of weir structures. These methods outperform standard empirical formulae and provide more exact results [25,[31][32][33][34].…”

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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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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“…For example, M5P, as a basic tree-based model, has been utilized in the assessment of Parshall flumes aeration efficiency Sangeeta & Tiwari, 2019) and proved to be highly efficient against alternative AI algorithms. As another novel algorithm, Extreme Machine Learning also proved to be a useful prediction tool for weirs discharge coefficient problems (Gharib et al, 2020;Yarmohammadi et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Machine learning model development for predicting aeration efficiency through Parshall flume

Sangeeta

Asadollah

Sharafati

et al. 2021

Engineering Applications of Computational Fluid Mechanics

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This study compares several advanced machine learning models to obtain the most accurate method for predicting the aeration efficiency (E 20 ) through the Parshall flume. The required dataset is obtained from the laboratory tests using different flumes fabricated in National Institute Technology Kurukshetra, India. Besides, the potential of K Nearest Neighbor (KNN), Random Forest Regression (RFR), and Decision Tree Regression (DTR) models are evaluated to predict the aeration efficiency. In this way, several input combinations (e.g. M1-M15) are provided using the laboratory parameters (e.g. W/L, S/L, Fr, and Re). Different predictive models are obtained based on those input combinations and machine learning models proposed in the present study. The predictive models are assessed based on several performance metrics and visual indicators. Results show that the KNN-M11 model (RMSE testing = 0.002, R 2 testing = 0.929), which includes W/L, S/L, and Fr as predictive variables outperforms the other predictive models. Furthermore, an enhancement is observed in KNN model estimation accuracy compared to the previously developed empirical models. In general, the predictive model dominated in the present study provides adequate performance in predicting the aeration efficiency in the Parshall flume.

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“…In the recent years GEP and learning machine were used to model of nonlinear problems of predicting discharge coefficient in side weir such as Isa et al (2015) and Reza et al (2020). The aim of this study is to estimate MLR equation and compare equation modeled from GEP for coefficient of discharge calculation from skew side weir and then compare these values with values of C d estimated from the rectangular side weir.…”

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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Simulation of discharge coefficient of side weirs placed on convergent canals using modern self-adaptive extreme learning machine

Cited by 17 publications

References 26 publications

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

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

Machine learning model development for predicting aeration efficiency through Parshall flume

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

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