Evaluation of conjugate depths of hydraulic jump in circular pipes using evolutionary computing

Najafzadeh, Mohammad

doi:10.1007/s00500-019-03877-9

Cited by 29 publications

(9 citation statements)

References 61 publications

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“…The correlation of the friction factor results obtained from the explicit approximation proposed and other explicit approximations are shown in this section. To evaluate explicit approximations performance in terms of statistical indices: maximum relative error (RE + , RE -), mean relative error (MRE), coeficient of determination (R 2 ), root mean square error (RMSE), scatter index (SI), Akaike information criterion (AIC), BIAS and index of agreement (IOA) can be defined as follows (Shaikh et al, 2015;Najafzadeh, 2019):…”

Section: Methodsmentioning

confidence: 99%

“…The validation of explicit correlations or approximations requires the handling of a large amount of data and traditionally statistical parameters are used to determine the precision of the fit between the estimated data and the observed data. In the works of Brkic (2011b) and Najafzadeh (2019), they used the error of the estimated data with respect to the observed data as a way of optimizing the fit. This method generally…”

Section: ( )mentioning

confidence: 99%

“…This numerical work has been a contribution to predict the value of the friction factor using conventional methods. However, in recent times authors such as Najafzadeh have made relevant contributions using Gene-Expression Programming (GEP), Evolutionary Polynomial Regression (EPR), Model Tree (MT), Neuro-Fuzzy GMDH to predict the behavior of fluids in different media and with very good results (Najafzadeh & Barani, 2011;Najafzadeh & Sattar, 2015;Najafzadeh & Kargar, 2019;Najafzadeh, 2019).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

New explicit correlation to compute the friction factor under turbulent flow in pipes

Olivares-Gallardo

Rojas

Guerra

2021

Rev. bras. eng. agríc. ambient.

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Section: Methodsmentioning

confidence: 99%

Section: ( )mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

New explicit correlation to compute the friction factor under turbulent flow in pipes

Olivares-Gallardo

Rojas

Guerra

2021

Rev. bras. eng. agríc. ambient.

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“…Additionally, model efficiency in the calibration and validation periods was evaluated using the Kling-Gupta efficiency criteria (KGE and KGE') [84], the root mean square error (RMSE) [71], the Nash-Sutcliffe efficiency criterion (NSE) [85], the index of agreement (IOA) [86], the mean absolute error (MAE) [86], the mean absolute percentage error (MAPE) [87], the scatter index (SI) [88] and BIAS [86,89].…”

Section: Model Efficiencymentioning

confidence: 99%

Comparison of Three Daily Rainfall-Runoff Hydrological Models Using Four Evapotranspiration Models in Four Small Forested Watersheds with Different Land Cover in South-Central Chile

Flores

Rodrı́guez

Yépez

et al. 2021

Water

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We used the lumped rainfall–runoff hydrologic models Génie Rural à 4, 5, 6 paramètres Journalier (GR4J, GR5J and GR6J) to evaluate the most robust model for simulating discharge on four forested small catchments (<40 ha) in south-central Chile. Different evapotranspiration methods were evaluated: Oudin, Hargreaves–Samani and Priestley–Taylor. Oudin’s model allows the achievement of the highest efficiencies in the flow simulation. The more sensitive parameters for each model were identified through a Generalized Probability Uncertainty Estimation (GLUE) model. Our results demonstrate that the three hydrological models were capable of efficiently simulating flow in the four study catchments. However, the GR6J model obtained the most satisfactory results in terms of simulated to measured streamflow closeness. In general, the three models tended to underestimate peak flow, as well as underestimate and overestimate flow events in most of the in situ observations, according to the probability of non-exceedance. We also evaluated the models’ performance in a simulation of summer discharge due to the importance of downstream water supply in the months of greatest scarcity. Again, we found that GR6J obtained the most efficient simulations.

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“…For instance, Sivapragasam et al [12] utilized genetic programming (GP) and the artificial neural network (ANN) to predict water surface profile as a steady flow with different discharges passes over a rectangular notch. Although AI models have been successfully used for solving numerous problems in water resources management and hydraulic engineering [18][19][20][21], it has not been applied to estimate the length of GVF profiles.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Artificial Intelligence Models for Estimating Lengths of Gradually Varied Flow Profiles

2021

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The study of water surface profiles is beneficial to various applications in water resources management. In this study, two artificial intelligence (AI) models named the artificial neural network (ANN) and genetic programming (GP) were employed to estimate the length of six steady GVF profiles for the first time. The AI models were trained using a database consisting of 5154 dimensionless cases. A comparison was carried out to assess the performances of the AI techniques for estimating lengths of 330 GVF profiles in both mild and steep slopes in trapezoidal channels. The corresponding GVF lengths were also calculated by 1-step, 3-step, and 5-step direct step methods for comparison purposes. Based on six metrics used for the comparative analysis, GP and the ANN improve five out of six metrics computed by the 1-step direct step method for both mild and steep slopes. Moreover, GP enhanced GVF lengths estimated by the 3-step direct step method based on three out of six accuracy indices when the channel slope is higher and lower than the critical slope. Additionally, the performances of the AI techniques were also investigated depending on comparing the water depth of each case and the corresponding normal and critical grade lines. Furthermore, the results show that the more the number of subreaches considered in the direct method, the better the results will be achieved with the compensation of much more computational efforts. The achieved improvements can be used in further studies to improve modeling water surface profiles in channel networks and hydraulic structure designs.

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Evaluation of conjugate depths of hydraulic jump in circular pipes using evolutionary computing

Cited by 29 publications

References 61 publications

New explicit correlation to compute the friction factor under turbulent flow in pipes

New explicit correlation to compute the friction factor under turbulent flow in pipes

Comparison of Three Daily Rainfall-Runoff Hydrological Models Using Four Evapotranspiration Models in Four Small Forested Watersheds with Different Land Cover in South-Central Chile

Assessment of Artificial Intelligence Models for Estimating Lengths of Gradually Varied Flow Profiles

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