scholarly journals A software tool to assess uncertainty in transient-storage model parameters using Monte Carlo simulations

2017 ◽  
Vol 36 (1) ◽  
pp. 195-217 ◽  
Author(s):  
Adam S. Ward ◽  
Christa A. Kelleher ◽  
Seth J. K. Mason ◽  
Thorsten Wagener ◽  
Neil McIntyre ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Software Tool ◽  
Transient Storage ◽  
Model Parameters ◽  
Storage Model ◽  
Transient Storage Model

scholarly journals Identifiability of transient storage model parameters along a mountain stream

2013 ◽  
Vol 49 (9) ◽  
pp. 5290-5306 ◽  
Author(s):  
C. Kelleher ◽  
T. Wagener ◽  
B. McGlynn ◽  
A. S. Ward ◽  
M. N. Gooseff ◽  
...  
Keyword(s):  
Transient Storage ◽  
Mountain Stream ◽  
Model Parameters ◽  
Storage Model ◽  
Transient Storage Model

Application of transient storage modelling to solute transport across a surcharged manhole

2007 ◽  
Vol 55 (4) ◽  
pp. 65-73 ◽  
Author(s):  
I. Guymer ◽  
R. Dutton
Keyword(s):  
Laboratory Experiments ◽  
Threshold Level ◽  
Transient Storage ◽  
Model Parameters ◽  
Storage Model ◽  
Storage Volume ◽  
Storage Zone ◽  
Jet Theory ◽  
Volume Retention ◽  
Transient Storage Model

Results from previous solute tracer laboratory experiments across circular surcharged manhole structures by Guymer et al. have been used to optimise parameters within Hart's transient storage model (TSM). A surcharge threshold level for the model parameters is evident and this is explained in relation to jet theory. The ability to decompose the TSM is demonstrated with reference to frequency of exchange with the storage zone allowing the proportions of solute entering these regions to be inferred, together with an indication of storage volume retention times.

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

Water ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 76
Author(s):  
Hyoseob Noh ◽  
Siyoon Kwon ◽  
Il Won Seo ◽  
Donghae Baek ◽  
Sung Hyun Jung
Keyword(s):  
Genetic Programming ◽  
Empirical Models ◽  
Influential Factor ◽  
Sensitivity Analyses ◽  
Transient Storage ◽  
Model Parameters ◽  
Flow Zone ◽  
Storage Model ◽  
Natural Rivers ◽  
Transient Storage Model

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.

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