Application of the Evolutionary Methods in Determining the Discharge Coefficient of Triangular Labyrinth Weirs

Asl, Mahdi Majedi; Fuladipanah, Mehdi

doi:10.29252/jstnar.22.4.279

Cited by 2 publications

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“…Mahmoud et al 37 concluded that the ANFIS-PSO and MLP-FA (multi-layer perceptron and firefly optimization algorithm) methods are the most accurate in estimating the C d of triangular labyrinth weirs, respectively. In a similar study, Majediasl and Fuladipanah 38 concluded that the SVM model produces the most exact results in predicting the C d of labyrinth weir with RMSE = 0.0118. Shafiei et al 21 reported that the ANFIS-FFA model is quite accurate in estimating the C d of the labyrinth weir.…”

Section: Resultsmentioning

confidence: 86%

“…Emami et al 44 predicted the C d of a curved plan labyrinth weirs using the WOA-ANFIS method, and the input parameters H/W and θ (weir arc angle) were introduced as the most effective parameters in estimating the C d . Majediasal and Fuladipanah 38 , investigated the support vector machine (SVM) method for C d of sharp-crested triangular labyrinth weirs and concluded that the input combination, including geometric parameters ( θ, h/w, L/B ), has the best results. Mohammadi et al 45 reported that the parameters H t /P , W/P (the ratio of the weir width to the height), R/W , W/LC (the ratio of the weir width to the effective length) as input variables have the most accuracy and efficiency in estimating the C d of U-shaped labyrinth weirs.…”

Section: Resultsmentioning

confidence: 99%

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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: Resultsmentioning

confidence: 86%

Section: Resultsmentioning

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

et al. 2021

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As a remarkable parameter, the discharge coefficient (Cd) plays an important role in determining weirs' passing capacity. In this research work, the SVM and the GEP algorithms were assessed to predict Cd of piano key weir (PKW), rectangular labyrinth weir (RLW), and trapezoidal labyrinth weir (TLW) with gathered experimental data set. Using dimensional analysis, various combinations of hydraulic and geometric non-dimensional parameters were extracted to perform simulation. The superior model for the SVM and the GEP predictor for PKW, RLW, and TLW included , and respectively. The results showed that both algorithms are potential in predicting discharge coefficient, but the coefficient of determination (RMSE, R2, Cd(DDR)max) illustrated the superiority of the GEP performance over the SVM. The results of the sensitivity analysis determined the highest effective parameters for PKW, RLW, and TLW in predicting discharge coefficients are , , and Fr respectively.

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Application of the Evolutionary Methods in Determining the Discharge Coefficient of Triangular Labyrinth Weirs

Cited by 2 publications

References 0 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

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

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