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

Hu, Zhenlong; Karami, Hojat; Rezaei, Alireza; DadrasAjirlou, Yashar; Piran, Md. Jalil; Band, Shahab S.; Chau, Kwok-wing; Mosavi, Amir

doi:10.1080/19942060.2021.1934546

Cited by 20 publications

(7 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Karami et al 10 showed that the ELM method with RMSE = 0.006 has acceptable efficiency in estimating the C d of the labyrinth weir. In a similar study, the effectiveness of the least-squares support vector machine-bat algorithm (LSSVM-BA) method was used to investigate the discharge of a curved labyrinth weir 39 . The results of the studies showed that the SVM-based model gave accurate results in estimating the C d of the arched labyrinth weir with values of RMSE = 0.013 and R 2 = 0.970 40 .…”

Section: Resultsmentioning

confidence: 99%

“…The increase in the effective length of the labyrinths at a specified width, due to the radius increases of PCLW1 and PCLW2 weirs causes an increase in the Cd. The studies showed that increasing the radius causes a reduction in eddy flows, turbulence, and a sudden increase in water height during the weir 39 , 40 , 42 . The results of the investigations showed that with the increase of R/W, the C d increases in the arched labyrinth weir, which is consistent with the results of the present study 41 .…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

LXGB: a machine learning algorithm for estimating the discharge coefficient of pseudo-cosine labyrinth weir

Emami

Parsa

2023

Sci Rep

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

LXGB: a machine learning algorithm for estimating the discharge coefficient of pseudo-cosine labyrinth weir

Emami

Parsa

2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Olyaie et al (2019) simulation indicated that the LSSVR could predict the Cd of the piano key (PK) weir accurately enough. The LSSVR was used to model the Cd of curved labyrinth overflows (Hu et al, 2021). They found that it could be a suitable tool for predicting Cd.…”

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

View full text Add to dashboard Cite

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.

show abstract

“…Zhu et al [17] conducted research on the surface wind pressure of low-rise buildings, obtained the surface wind pressure under different wind forces by using numerical simulation methods, established a surrogate model based on machine learning, and applied it to optimize the placement of building surface pressure sensors. Hu et al [18] used adaptive neural-fuzzy inference system, support vector machine, M5 model tree, least-squares support vector machine, and other intelligent prediction models to predict the overflow coefficient of curved pipelines, establishing the mapping relationship between the upstream water head, overflow ratio, curvature, and overflow coefficient. Wakes et al [19] used machine-learning algorithms to predict dune movement patterns under different wind conditions, providing a technical reference for predicting sediment migration paths.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Prediction Model for Local Resistance Coefficient of Water Transmission Tunnel Maintenance Ventilation Based on Machine Learning

Tong

Liu

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

Multiple ducts in the working shaft and main body of tunnels form a combined tee structure. An efficient and accurate prediction method for the local resistance coefficient is the key to the design and optimization of the maintenance ventilation scheme. However, most existing studies use numerical simulations and model experiments to analyze the local resistance characteristics of specific structures and calculate the local resistance coefficient under specific ventilation conditions. Therefore, there are shortcomings of low efficiency and high cost in the ventilation scheme optimization when considering the influence of the local resistance. This paper proposes a hybrid prediction model for the local resistance coefficient of water transmission tunnel maintenance ventilation based on machine learning. The hybrid prediction model introduces the hybrid kernel into a relevance vector machine to build the hybrid kernel relevance vector machine model (HKRVM). The improved artificial jellyfish search algorithm (IAJS), which utilizes Fuch chaotic mapping, lens-imaging reverse learning, and adaptive hybrid mutation strategies to improve the algorithm performance, is applied to the kernel parameter optimization of the HKRVM model. The results of a case study show that the method proposed in this paper can achieve the efficient and accurate prediction of the local resistance coefficient of maintenance ventilation and improve the prediction accuracy and prediction efficiency to a certain extent. The method proposed in this paper provides a new concept for the prediction of the ventilation local resistance coefficient and can further provide an efficient prediction method for the design and optimization of maintenance ventilation schemes.

show abstract

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

Cited by 20 publications

References 38 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

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

A Hybrid Prediction Model for Local Resistance Coefficient of Water Transmission Tunnel Maintenance Ventilation Based on Machine Learning

Contact Info

Product

Resources

About