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Profile photo of Ronny Berndtsson

Ronny Berndtsson

Professor, Dep Director, MECW Dep Scientific Coordinator

Profile photo of Ronny Berndtsson

Evolutionary polynomial regression approach to predict longitudinal dispersion coefficient in rivers

Author

  • Mohammad Rezaie Balf
  • Roohollah Noori
  • Ronny Berndtsson
  • Alireza Ghaemi
  • Behzad Ghiasi

Summary, in English

The fate of pollutants in rivers is mainly affected by the longitudinal dispersion coefficient (Kx). Thus, improved Kx estimation could greatly enhance the water quality management of rivers. In this regard, evolutionary polynomial regression (EPR) was used to accurately predict Kx in rivers as a function of flow depth, channel width, and average and shear velocities. The predicted Kx by EPR modelling was compared with results obtained by more conventional Kx estimation formulas. Initial data analyses using general linear models of variance revealed that all input variables were statistically significant for Kx estimation. The calibrated EPR model showed good performance with coefficient of determination and root mean square error of 0.82 and 79 m2/s, respectively. This is better that other more conventional estimation methods. Application of sensitivity analysis for the EPR model indicated that channel width, average velocity, shear velocity, and flow depth were the main variables in descending order that affected Kx variability. The introduced EPR estimation model for Kx can be incorporated in one-dimensional water quality models for improved simulation of solute concentration in natural rivers.

Department/s

  • Division of Water Resources Engineering
  • Centre for Advanced Middle Eastern Studies (CMES)
  • MECW: The Middle East in the Contemporary World

Publishing year

2018

Language

English

Pages

447-457

Publication/Series

Journal of Water Supply: Research and Technology - AQUA

Volume

67

Issue

5

Document type

Journal article

Publisher

IWA Publishing

Topic

  • Ocean and River Engineering

Keywords

  • Dispersion coefficient
  • Evolutionary polynomial regression
  • Pollution transport
  • Rivers
  • Sensitivity analysis

Status

Published

ISBN/ISSN/Other

  • ISSN: 1606-9935