Estimation of Transverse Mixing Coefficient in Straight and Meandering Streams

Mohammad Aghababaei (), Amir Etemad-Shahidi (), Ebrahim Jabbari () and Milad Taghipour ()
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Mohammad Aghababaei: Iran University of Science and Technology
Amir Etemad-Shahidi: Griffith University
Ebrahim Jabbari: Iran University of Science and Technology
Milad Taghipour: Poly technique Montréal

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2017, vol. 31, issue 12, No 8, 3809-3827

Abstract: Abstract Transverse mixing coefficient (TMC) is one of the key factors in the modelling of lateral dispersion of pollutants. Several researchers have attempted to estimate this coefficient using various models. However, robust equations that can accurately estimate lateral mixing in both straight and meandering streams are still required. In this study, novel formulae were developed using the hydraulic and geometric parameters of rivers. The multiple linear regression (MLR), genetic programming based symbolic regression (GPSR) and dimensionless parameters were used for this purpose. Two extensive data sets including data from straight channels/streams and meandering ones were employed to develop the formulae. The main advantage of the developed formula for meandering streams is proper consideration of the effects of aspect ratio, friction, and sinuosity. The formulae performances were then compared quantitatively with those of existing ones using accuracy metrics such as RMSE (Root Mean Square Error). The results illustrated that the proposed formulae outperform others in terms of accuracy and can be used for estimating TMC in straight and meandering streams. In addition, the comparison of MLR and GPSR models showed that the latter is marginally more accurate than MLR specially in meandering streams. However, the MLR models presented a more justifiable relationship between the TMC and governing dimensionless parameters. The main advantages of the presented formulae are that they are more accurate than previous models, can be used in both meandering and straight streams; and can be easily implemented in numerical models to estimate the pollutant concentration and mixing length.

Keywords: Transverse mixing; Genetic programming based symbolic regression; Straight streams; Meandering streams; Sinuosity (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s11269-017-1708-4

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