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Article

Multi-Gene Genetic Programming Regression Model for Prediction of Transient Storage Model Parameters in Natural Rivers

1
Department of Civil and Environmental Engineering, Seoul National University, Seoul 08826, Korea
2
Korea Institute of Civil Engineering and Building Technology, 283, Goyangdae-ro, Ilsanseo-gu, Goyang-si 10223, Gyeonggi-do, Korea
*
Author to whom correspondence should be addressed.
Water 2021, 13(1), 76; https://doi.org/10.3390/w13010076
Received: 25 November 2020 / Revised: 23 December 2020 / Accepted: 25 December 2020 / Published: 31 December 2020
(This article belongs to the Special Issue Contaminant Transport and Fate)
A Transient Storage Model (TSM), which considers the storage exchange process that induces an abnormal mixing phenomenon, has been widely used to analyze solute transport in natural rivers. The primary step in applying TSM is a calibration of four key parameters: flow zone dispersion coefficient (Kf), main flow zone area (Af), storage zone area (As), and storage exchange rate (α); by fitting the measured Breakthrough Curves (BTCs). In this study, to overcome the costly tracer tests necessary for parameter calibration, two dimensionless empirical models were derived to estimate TSM parameters, using multi-gene genetic programming (MGGP) and principal components regression (PCR). A total of 128 datasets with complete variables from 14 published papers were chosen from an extensive meta-analysis and were applied to derivations. The performance comparison revealed that the MGGP-based equations yielded superior prediction results. According to TSM analysis of field experiment data from Cheongmi Creek, South Korea, although all assessed empirical equations produced acceptable BTCs, the MGGP model was superior to the other models in parameter values. The predicted BTCs obtained by the empirical models in some highly complicated reaches were biased due to misprediction of Af. Sensitivity analyses of MGGP models showed that the sinuosity is the most influential factor in Kf, while Af, As, and α, are more sensitive to U/U*. This study proves that the MGGP-based model can be used for economic TSM analysis, thus providing an alternative option to direct calibration and the inverse modeling initial parameters. View Full-Text
Keywords: hydromorphic variable; Multigene Genetic Programming (MGGP); sensitivity analysis; solute transport; Transient Storage Model (TSM); TSM parameter estimation hydromorphic variable; Multigene Genetic Programming (MGGP); sensitivity analysis; solute transport; Transient Storage Model (TSM); TSM parameter estimation
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MDPI and ACS Style

Noh, H.; Kwon, S.; Seo, I.W.; Baek, D.; Jung, S.H. Multi-Gene Genetic Programming Regression Model for Prediction of Transient Storage Model Parameters in Natural Rivers. Water 2021, 13, 76. https://doi.org/10.3390/w13010076

AMA Style

Noh H, Kwon S, Seo IW, Baek D, Jung SH. Multi-Gene Genetic Programming Regression Model for Prediction of Transient Storage Model Parameters in Natural Rivers. Water. 2021; 13(1):76. https://doi.org/10.3390/w13010076

Chicago/Turabian Style

Noh, Hyoseob; Kwon, Siyoon; Seo, Il W.; Baek, Donghae; Jung, Sung H. 2021. "Multi-Gene Genetic Programming Regression Model for Prediction of Transient Storage Model Parameters in Natural Rivers" Water 13, no. 1: 76. https://doi.org/10.3390/w13010076

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