Evolutionary polynomial regression approach to predict longitudinal dispersion coefficient in rivers
Balf, Mohammad Rezaie ; Noori, Roohollah LU ; Berndtsson, Ronny LU
; Ghaemi, Alireza
and Ghiasi, Behzad
(2018)
In Journal of Water Supply: Research and Technology - AQUA
67(5).
p.447-457
- Abstract
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... (More)
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.
(Less)
- author
- Balf, Mohammad Rezaie
; Noori, Roohollah
LU
; Berndtsson, Ronny
LU
; Ghaemi, Alireza
and Ghiasi, Behzad
- organization
- publishing date
- 2018
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Dispersion coefficient, Evolutionary polynomial regression, Pollution transport, Rivers, Sensitivity analysis
- in
- Journal of Water Supply: Research and Technology - AQUA
- volume
- 67
- issue
- 5
- pages
- 11 pages
- publisher
- IWA Publishing
- external identifiers
-
- scopus:85053433014
- ISSN
- 1606-9935
- DOI
- 10.2166/aqua.2018.021
- language
- English
- LU publication?
- yes
- id
- 73ba6e7a-4e6b-44a1-baec-5707495a266e
- date added to LUP
- 2018-10-17 15:43:47
- date last changed
- 2023-09-08 09:05:14
@article{73ba6e7a-4e6b-44a1-baec-5707495a266e,
abstract = {{<p>The fate of pollutants in rivers is mainly affected by the longitudinal dispersion coefficient (K<sub>x</sub>). Thus, improved K<sub>x</sub> estimation could greatly enhance the water quality management of rivers. In this regard, evolutionary polynomial regression (EPR) was used to accurately predict K<sub>x</sub> in rivers as a function of flow depth, channel width, and average and shear velocities. The predicted K<sub>x</sub> by EPR modelling was compared with results obtained by more conventional K<sub>x</sub> estimation formulas. Initial data analyses using general linear models of variance revealed that all input variables were statistically significant for K<sub>x</sub> estimation. The calibrated EPR model showed good performance with coefficient of determination and root mean square error of 0.82 and 79 m<sup>2</sup>/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 K<sub>x</sub> variability. The introduced EPR estimation model for K<sub>x</sub> can be incorporated in one-dimensional water quality models for improved simulation of solute concentration in natural rivers.</p>}},
author = {{Balf, Mohammad Rezaie and Noori, Roohollah and Berndtsson, Ronny and Ghaemi, Alireza and Ghiasi, Behzad}},
issn = {{1606-9935}},
keywords = {{Dispersion coefficient; Evolutionary polynomial regression; Pollution transport; Rivers; Sensitivity analysis}},
language = {{eng}},
number = {{5}},
pages = {{447--457}},
publisher = {{IWA Publishing}},
series = {{Journal of Water Supply: Research and Technology - AQUA}},
title = {{Evolutionary polynomial regression approach to predict longitudinal dispersion coefficient in rivers}},
url = {{http://dx.doi.org/10.2166/aqua.2018.021}},
doi = {{10.2166/aqua.2018.021}},
volume = {{67}},
year = {{2018}},
}