Mohammad Najafzadeh, Roohollah Noori, Diako Afroozi, Behzad Ghiasi, Seyed-Mohammad Hosseini-Moghari, Ali Mirchi, Ali Torabi Haghighi, Bjørn Kløve, A comprehensive uncertainty analysis of model-estimated longitudinal and lateral dispersion coefficients in open channels, Journal of Hydrology, Volume 603, Part A, 2021, 126850, ISSN 0022-1694, https://doi.org/10.1016/j.jhydrol.2021.126850
A comprehensive uncertainty analysis of model-estimated longitudinal and lateral dispersion coefficients in open channels
|Author:||Najafzadeh, Mohammad1; Noori, Roohollah2,3; Afroozi, Diako1;|
1Department of Water Engineering, Faculty of Civil and Surveying Engineering, Graduate University of Advanced Technology, 76315-116 Kerman, Iran
2Water, Energy and Environmental Engineering Research Unit, Faculty of Technology, University of Oulu, 90014 Oulu, Finland
3School of Civil Engineering, Iran University of Science and Technology, Narmak, Tehran, 1684613114, Iran
4School of Environment, College of Engineering, University of Tehran, 1417853111 Tehran, Iran
5Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
6Department of Biosystems and Agricultural Engineering, Oklahoma State University, 111 Agricultural Hall, Stillwater, OK 74078, USA
|Online Access:||PDF Full Text (PDF, 1 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021110854253
|Publish Date:|| 2021-11-08
The complexity of pollutant-mixing mechanism in open channels generates large uncertainty in estimation of longitudinal and lateral dispersion coefficients (Kx and Ky). Therefore, Kx and Ky estimation in rivers should be accompanied by an uncertainty analysis, a subject mainly ignored in previous studies. We introduce a method based on thorough analysis of different calibration datasets, resampled from a global database of tracer studies, to determine the uncertainty associated with five applicable intelligent models for estimation of Kx and Ky (model tree, evolutionary polynomial regression (EPR), gene-expression programming, multivariate adaptive regression splines (MARS), and support vector machine (SVM)). Our findings suggest that SVM gives least uncertainty in both Kx and Ky estimation, while EPR and MARS generate most uncertainty in Kx and Ky estimation, respectively. By considering significant uncertainty in the model estimations, we suggest that the methodology we introduce here for uncertainty determination of the models be incorporated in empirical studies on estimation of Kx and Ky in rivers.
Journal of hydrology
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
218 Environmental engineering
The first author acknowledges the support from the Arctic Interactions (ArcI) Visit Grant program, Profi 4, University of Oulu.
© 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).