Using data mining methods to improve discharge coefficient prediction in Piano Key and Labyrinth weirs

Asl, Mahdi Majedi; Fuladipanah, Mehdi; Arun, Venkat; Tripathi, Ravi Prakash

doi:10.2166/ws.2021.304

Cited by 10 publications

(6 citation statements)

References 31 publications

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“…For this purpose, SVM (support vector machines) and the GEP (gene expression programming) models were used. They reported that the GEP model results are in a good agreement with the experimental data [23]. In another study, the discharge coefficient of oblique sluice gates was assessed using some different techniques and the ANN (artificial neural network) approach was introduced as the most accurate model [24].…”

Section: Introductionmentioning

confidence: 78%

“…The Correlation Coefficient (CC), Root Mean Square Error (RMSE), Willmott's Index (WI), Legates and McCabe's Index (LMI), Mean Absolute Error (MAE), efficiency of the Nash Sutcliffe model (NS), Normalized Root Mean Square Error (NRMSE), and Root Mean Square Relative Error (RMSRE) were used for assessing the training and testing phases of prediction models [40][41][42][43][44][45][46][47][48][49]. It is possible to quantify the six performance assessment parameters used in this research using Equations ( 17)- (23).…”

Section: Parameters For Performance Appraisalmentioning

confidence: 99%

“…They used 285 experimental data to test the developed model and the outcomes of the research revealed that the ANFIS could be effectively used in practice. Piano Key weirs' C d was investigated by Majedi-Asl et al [23]. For this purpose, SVM (support vector machines) and the GEP (gene expression programming) models were used.…”

Section: Introductionmentioning

confidence: 99%

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Approximation of the Discharge Coefficient of Radial Gates Using Metaheuristic Regression Approaches

et al. 2022

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Radial gates are widely used for agricultural water management, flood controlling, etc. The existence of methods for the calculation of the discharge coefficient (Cd) of such gates are complex and they are based on some assumptions. The development of new usable and simple models is needed for the prediction of Cd. This study investigates the viability of a metaheuristic regression method, the Gaussian Process (GP), for the determination of the discharge coefficient of radial gates. For this purpose, a total of 2536 experimental data were compiled that cover a wide range of all the effective parameters. The results of GP were compared with the Group Method of Data Handling (GMDH), Multivariate Adaptive Regression Splines (MARS), and linear and nonlinear regression models for predicting Cd of radial gates in both free-flow and submerged-flow conditions. The results revealed that the radial basis function-based GP model performed the best in free-flow condition with a Correlation Coefficient (CC) of 0.9413 and Root Mean Square Error (RMSE) of 0.0190 while the best accuracy was obtained from the Pearson VII kernel function-based GP model for the submerged flow condition with a CC of 0.9961 and RMSE of 0.0132.

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Section: Introductionmentioning

confidence: 78%

Section: Parameters For Performance Appraisalmentioning

confidence: 99%

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Approximation of the Discharge Coefficient of Radial Gates Using Metaheuristic Regression Approaches

et al. 2022

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“…However, when the flow pattern is turbulent or the flow pattern is rapidly changing, the individual probe will not be able to accurately obtain the path flow velocity, and the flow velocity area method will produce large errors when used for flow calculation. Due to the lack of a professional platform for the detection and testing of measurement and control gates, few studies have been carried out to investigate the calibration method for flow in the measurement box of the measurement and control gate [22,23]. It is very important to calibrate the accuracy of the path flow velocity measured by the probe inside the measurement box during the development and debugging process of the measurement and control gate.…”

Section: Discussionmentioning

confidence: 99%

Flow Prediction of a Measurement and Control Gate Based on an Optimized Back Propagation Neural Network

Hou,

Niu,

Zhu

et al. 2023

Applied Sciences

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The measurement and control gate, as a new type of measurement and control equipment, has been widely used for water quantity control in irrigation areas. However, there is a lack of methods for calibrating the flow inside the measurement box at present. This paper establishes a flow prediction model based on a back propagation (BP) neural network and its optimization algorithm by using 450 sets of sample data obtained from the indoor gate overflow test and verified the effectiveness and accuracy of the prediction model by using another 205 sets of sample data. The results show that the gate flow prediction model based on a BP neural network and its optimization algorithm has self-adaptability to different flow patterns, and its prediction accuracy is significantly higher than that of the traditional water measurement method. Compared to the unoptimized BP model, the BP model optimized by the genetic algorithm (GA) or particle swarm optimization (PSO) has higher prediction accuracy and better error distribution. Both GA and PSO algorithms can be used to optimize the initial weights and thresholds of the BP flow prediction model. However, by comprehensively analyzing the prediction accuracy, error distribution, and running time, the PSO algorithm has better optimization performance compared to the GA algorithm. The prediction model can provide a reference for flow rate calibration and the anomaly rejection of measurement and control gates in the irrigation area.

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“…In the initial set of experiments, two unprotected abutments were tested, and it was observed that the highest scour depth occurred at the abutment toe. Therefore, the abutments toe was chosen as the reference point for comparison of the scouring in all subsequent experiments, as it was in similar studies [28,46,47]. The topography around the abutment was recorded in a 2 × 2 cm grid using a laser distance meter that could move in longitudinal and cross-sectional directions with 1 mm accuracy.…”

Section: Methodsmentioning

confidence: 99%

Experimental Study on the Optimum Installation Depth and Dimensions of Roughening Elements on Abutment as Scour Countermeasures

Zolghadr

Zomorodian

Fathi

et al. 2023

Fluids

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The causes of many bridge failures have been reported to be local scour around abutments. This study examines roughening elements as devices with which to intercept the downflow responsible for the formation of the principal vortex, which is what triggers local scour around abutments. Two vertical wall abutments with different widths were examined under four different hydraulic conditions in a clear-water regime. Elements with different thicknesses (t) and protrusions (P) with the same dimensions, (P = t = 0.05 L, 0.1 L, 0.2 L, and 0.3 L, where L is the length of the abutment) and with varying depths of installation (Z) were considered. Elements were installed in two positions: between the sediment surface and water elevation and buried within the sediment. To determine the optimum depth of installation, one element was first installed on the sediment surface, and the number of elements was increased in each subsequent test. The results show that installing elements between water surface elevation and the sediment’s initial level did not show any defined trend on scour depth reduction. However, the optimum installation depth of the elements is 0.6–0.8 L below the initial bed level. Moreover, the roughening elements with thickness and protrusion of P = t = 0.2 L resulted in the most effective protection of the foundation. The best arrangement, (P = t = 0.2 L and Z = >0.6–0.8 L) reduced the maximum scour depth by up to 30.4% and 32.8% for the abutment with smaller and larger widths, respectively.

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Using data mining methods to improve discharge coefficient prediction in Piano Key and Labyrinth weirs

Cited by 10 publications

References 31 publications

Approximation of the Discharge Coefficient of Radial Gates Using Metaheuristic Regression Approaches

Approximation of the Discharge Coefficient of Radial Gates Using Metaheuristic Regression Approaches

Flow Prediction of a Measurement and Control Gate Based on an Optimized Back Propagation Neural Network

Experimental Study on the Optimum Installation Depth and Dimensions of Roughening Elements on Abutment as Scour Countermeasures

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