Recalibration of a Reaeration Equation

Thackston, Edward L.; Dawson, J. W.

doi:10.1061/(asce)0733-9372(2001)127:4(317)

Cited by 18 publications

(13 citation statements)

References 10 publications

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“…The scatter diagram presented in Fig. 3 also shows that the scatter between the observed and modelled values is far less for the ANN than for the approach by Thackston & Dawson (2001). It can be seen that the ANN performs very well for lower K 2 , but for high K 2 it underestimates values.…”

Section: Analysis and Resultsmentioning

confidence: 83%

“…However, overestimation of K 2 may mislead the water resources planners into removal of pollutants and expenditure involved in its treatment. Table 4 Performance evaluation of re-aeration equations for data sets used by Thackston & Dawson (2001) The data set used by Thackston & Dawson (2001) was also analysed in this study. However, not all the data used by them were available to the authors-only 184 patterns were available.…”

Section: Analysis and Resultsmentioning

confidence: 99%

“…Out of the data sets, 400 randomly selected patterns were used for training and 176 for testing. To estimate the re-aeration coefficient, some empirical equations (Thackston & Dawson, 2001;Moog & Jirka, 1998;Parkhurst & Pomeroy, 1969) and an ANN were used. Note that the equation developed by Moog & Jirka (1998) is the only equation based on slope criteria; the other equations are not based on any specific criteria.…”

Section: Analysis and Resultsmentioning

confidence: 99%

“…These were collected from published literature (O'Connor & Dobbins, 1958;Churchill et al, 1962;Thackston & Krenkel, 1969;Parkhurst & Pomeroy, 1972;Hren, 1984;Wilcock, 1988). The data given by Ruhl & Smoot (1987) and Parker & DeSimone (1992) were used by Thackston & Dawson (2001), but were not available to the authors.…”

Section: Data Sets Obtained From the Literaturementioning

confidence: 99%

“…They concluded that slope is an essential component of re-aeration equations. Furthermore, Thackston & Dawson (2001) revised the reaeration equation and claimed that the new equation forms or new parameter values provide better results for all the data sets used by Moog & Jirka (1998). They also suggested that the empirical re-aeration equations should not be applied for very high Froude numbers where the influence of turbulent water is prevalent.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Comparing the stream re-aeration coefficient estimated from ANN and empirical models / Comparaison d'estimations par un RNA et par des modèles empiriques du coefficient de réaération en cours d'eau

Jain

Jha²

2005

Hydrological Sciences Journal

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Dissolved oxygen (DO) is one of the most useful indices of river's health and the stream re-aeration coefficient is an important input to computations related to DO. Normally, this coefficient is expressed as a function of several variables, such as mean stream velocity, shear stress velocity, bed slope, flow depth, and Froude number. However, in free surface flows, some of these variables are interrelated, and it is possible to obtain simplified stream re-aeration equations. In recent years, different functional forms have been advanced to represent the re-aeration coefficient for different data sets. In the present study, the artificial neural network (ANN) technique has been applied to estimate the re-aeration coefficient (K 2 ) using data sets measured at different reaches of the Kali River in India and values obtained from the literature. Observed stream/channel velocity, bed slope, flow depth, cross-sectional area and re-aeration coefficient data were used for the analysis. Different combinations of variables were tested to obtain the re-aeration coefficient using an ANN. The performance of the ANN was compared with other estimation methods. It was found that the re-aeration coefficient estimated by using an ANN was much closer to the observed values as compared with the other techniques.

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Section: Analysis and Resultsmentioning

confidence: 83%

Section: Analysis and Resultsmentioning

confidence: 99%

Section: Analysis and Resultsmentioning

confidence: 99%

Section: Data Sets Obtained From the Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Comparing the stream re-aeration coefficient estimated from ANN and empirical models / Comparaison d'estimations par un RNA et par des modèles empiriques du coefficient de réaération en cours d'eau

Jain

Jha²

2005

Hydrological Sciences Journal

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2021

Water‐Quality Engineering in Natural Systems

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Evaluation of various relationships of reaeration rate coefficient for modeling dissolved oxygen in a river with extreme flow variations in Pakistan

Haider

Ali

Haydar

2012

Hydrological Processes

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Mathematical relationships have been developed for reaeration rate coefficient (Ka) by various researchers. These relationships have a number of variables such as depth, velocity, width, slope, Froud number, molecular diffusion coefficient, kinematic viscosity and the gas‐transfer Reynolds number. From these variables, 29 relations have been developed and divided into four groups. To evaluate their predictive capability for highly variable flow rivers receiving high pollution loads form large cities, these relationships have been used to model dissolved oxygen (DO) in the River Ravi. Such rivers are either saturated with DO during high flows or anaerobic during critical low‐flow conditions. The evaluation is based on the agreement between model DO values calculated using Ka obtained from the available equations and the measured DO concentrations in the river samples in terms of sum of square of residuals (SSR) and coefficient of determination (R2). It has been found that in general, the group of equations containing depth and velocity as the only two variables affecting Ka performed better than the equations in other groups as reflected by lower SSR and higher R2 values. The study results also reveal that the turbulence‐based reaeration rate coefficient equation containing additional variables also resulted in close agreement between DO model results and the measured values. The study results identify the most important parameters affecting the reaeration rate coefficient and the suitability of various Ka relationships as well for rivers with highly variable flows. Copyright © 2012 John Wiley & Sons, Ltd.

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Recalibration of a Reaeration Equation

Cited by 18 publications

References 10 publications

Comparing the stream re-aeration coefficient estimated from ANN and empirical models / Comparaison d'estimations par un RNA et par des modèles empiriques du coefficient de réaération en cours d'eau

Comparing the stream re-aeration coefficient estimated from ANN and empirical models / Comparaison d'estimations par un RNA et par des modèles empiriques du coefficient de réaération en cours d'eau

Bibliography

Evaluation of various relationships of reaeration rate coefficient for modeling dissolved oxygen in a river with extreme flow variations in Pakistan

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