A novel approach for estimation of discharge coefficient in broad-crested weirs based on Harris Hawks Optimization algorithm

Nourani, Bahram; Arvanaghi, Hadi; Salmasi, Farzin

doi:10.1016/j.flowmeasinst.2021.101916

Cited by 23 publications

(3 citation statements)

References 21 publications

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“…achieved by these new metaheuristic algorithms can be attributed to their effective balance between exploration and exploitation, allowing them to avoid getting stuck in local optima. These findings align with previous studies that have reported enhanced accuracy in models such as MLP, least square support vector regression, radial basis function, and adaptive neuro-fuzzy inference system when employing metaheuristic training techniques (Hu et al 2021;Sihag et al 2021;Nourani et al 2021).…”

Section: Comparison Of Different Modelssupporting

confidence: 91%

Multi-approach assessment for predicting submerged discharge reduction factor in porous broad-crested weirs

Seif,

Arman,

Rahmanshahi

2024

Preprint

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Environmentally friendly porous weirs have attracted the attention of researchers and engineers due to their favorable characteristics, surpassing solid weirs in terms of environmental impact, hydraulic performance, and stability. However, accurately estimating the submerged discharge coefficient for porous weirs is challenging due to the complex flow mechanisms involved, particularly under submerged conditions. The discharge under submerged conditions is typically expressed as a multiple of the free flow discharge, along with a coefficient representing the submerged discharge reduction factor (SDRF). This study aims to propose a novel artificial intelligence framework that incorporates metaheuristic techniques to predict SDRF for porous broad-crested weirs (PBCWs). The research utilized generalized normal distribution optimization (GNDO) to optimize the multilayer perceptron (MLP) model, enabling more precise predictions. The performance of the hybrid MLP-GNDO model was compared to that of an MLP, gene-expression programming (GEP), and standard nonlinear regression (SNR) models. A dataset comprising 966 observed experiments was employed to evaluate the proposed models. The results demonstrated that the hybrid MLP-GNDO model outperformed the MLP, GEP, and SR models, achieving a root mean square error of 0.021 and 0.022 and an R2 value of 0.964 and 0.954 for the training and test datasets, respectively. This model accurately predicted the train and test datasets with an average error rate of less than 2%. Regarding accuracy, the models ranked in the following order: MLP, GEP, and SNR.

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Section: Comparison Of Different Modelssupporting

confidence: 91%

Multi-approach assessment for predicting submerged discharge reduction factor in porous broad-crested weirs

Seif,

Arman,

Rahmanshahi

2024

Preprint

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“…The modelling results of a Cd radial gate indicated that the GPR attained acceptable predictable performance (Tao et al, 2022). Nourani et al (2021) estimated the Cd over broad-crested weirs using the GPR.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty analysis of discharge coefficient predicted for rectangular side weir using machine learning methods

Seyedian,

Kisi

2024

Journal of Hydrology and Hydromechanics

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The present study used three machine learning models, including Least Square Support Vector Regression (LSSVR) and two non-parametric models, namely, Quantile Regression Forest (QRF) and Gaussian Process Regression (GPR), to quantify uncertainty and precisely predict the side weir discharge coefficient (Cd) in rectangular channels. So, 15 input structures were examined to develop the models. The results revealed that the machine learning models used in the study offered better accuracy compared to the classical equations. While the LSSVR and QRF models provided a good prediction performance, the GPR slightly outperformed them. The best input structure that was developed included all four dimensionless parameters. Sensitivity analysis was conducted to identify the effective parameters. To evaluate the uncertainty in the predictions, the LSSVR, QRF, and GPR were used to generate prediction intervals (PI), which quantify the uncertainty coupled with point prediction. Among the implemented models, the GPR and LSSVR models provided more reliable results based on PI width and the percentage of observed data covered by PI. According to point prediction and uncertainty analysis, it was concluded that the GPR model had a lower uncertainty and could be successfully used to predict Cd.

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“…Comparisons showed that results from the standard k-ω turbulence model were in better agreement with measured values. Haun et al (2011) applied Flow 3D and SSIIM2 to calculate the discharge coefficient over a trapezoidal broad-crested weir and Nourani et al (2021) numerically estimated the discharge coefficients (Cd) over the broad-crested weirs with different configurations. In addition, they utilized two intelligent models ANN, GPR and the hybrid models ANN-HHO, GPR-HHO to determine the discharge coefficients as accurately as possible.…”

Section: Introductionmentioning

confidence: 99%

Flow over embankment gabion weirs in free flow conditions

Biabani

Salmasi

Nouri

et al. 2022

Journal of Hydro-environment Research

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A novel approach for estimation of discharge coefficient in broad-crested weirs based on Harris Hawks Optimization algorithm

Cited by 23 publications

References 21 publications

Multi-approach assessment for predicting submerged discharge reduction factor in porous broad-crested weirs

Multi-approach assessment for predicting submerged discharge reduction factor in porous broad-crested weirs

Uncertainty analysis of discharge coefficient predicted for rectangular side weir using machine learning methods

Flow over embankment gabion weirs in free flow conditions

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