Interpolation Accuracy of Hybrid Soft Computing Techniques in Estimating Discharge Capacity of Triangular Labyrinth Weir

Mahmoud, Ali; Yuan, Xiao; Kheimi, Marwan; Yuan, Yanbin

doi:10.1109/access.2021.3049223

Cited by 17 publications

(2 citation statements)

References 33 publications

(75 reference statements)

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“…The results show the accuracy of the presented LXGB approach in estimating the C d of PCLW1 and PCLW2 models of PCLW. 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.…”

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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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: 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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“…The outcomes indicated that the proposed methods demonstrated higher performance in the training and testing stages. In another work, Mahmoud et al (2021b) estimated the flowrate of a sharp-crest triangular labyrinth weir as a function of its side leg angle α and total head ratio (H/P) through several soft computing techniques. They concluded that the competence of soft computing techniques in the testing stage cannot guarantee their accuracy in the interpolation task.…”

Section: Introductionmentioning

confidence: 99%

Using soft computing and machine learning algorithms to predict the discharge coefficient of curved labyrinth overflows

Karami

Rezaei

et al. 2021

Engineering Applications of Computational Fluid Mechanics

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This research aims to estimate the overflow capacity of a curved labyrinth using different intelligent prediction models, namely the adaptive neural-fuzzy inference system, the support vector machine, the M5 model tree, the least-squares support vector machine and the least-squares support vector machine-bat algorithm (LSSVM-BA). A total of 355 empirical data for 6 different congressional overflow models were extracted from the results of a laboratory study on labyrinth overflow models. The parameters of the upstream water head to overflow ratio, the lateral wall angle and the curvature angle were used to estimate the discharge coefficient of curved labyrinth overflows. Based on various statistical evaluation indicators, the results show that those input parameters can be relied upon to predict the discharge coefficient. Specifically, the LSSVM-BA model showed the best prediction accuracy during the training and test phases. Such a low-cost prediction model may have a remarkable practical implication as it could be an economic alternative to the expensive laboratory solution, which is costly and time-consuming.

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A Walnut optimization algorithm applied to discharge coefficient prediction on labyrinth weirs

Emami

Parsa

2022

Soft Comput

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Interpolation Accuracy of Hybrid Soft Computing Techniques in Estimating Discharge Capacity of Triangular Labyrinth Weir

Cited by 17 publications

References 33 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 soft computing and machine learning algorithms to predict the discharge coefficient of curved labyrinth overflows

A Walnut optimization algorithm applied to discharge coefficient prediction on labyrinth weirs

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