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A global and efficient multi-objective auto-calibration and uncertainty estimation method for water quality catchment models

A. van Griensven and T. Meixner (2007) JOURNAL OF HYDROINFORMATICS. 9(4). p.277-291
abstract
Catchment water quality models have many parameters, several output variables and a complex structure leading to multiple minima in the objective function. General uncertainty/optimization methods based on random sampling (e.g. GLUE) or local methods (e.g. PEST) are often not applicable for theoretical or practical reasons. This paper presents "ParaSol", a method that performs optimization and uncertainty analysis for complex models such as distributed water quality models. Optimization is done by adapting the Shuffled Complex Evolution algorithm (SCE-UA) to account for multi-objective problems and for large numbers of parameters. The simulations performed by the SCE-UA are used further for uncertainty analysis and thereby focus the uncertainty analysis on solutions near the optimum/optima. Two methods have been developed that select "good" results out of these simulations based on an objective threshold. The first method is based on chi(2) statistics to delineate the confidence regions around the optimum/optima and the second method uses Bayesian statistics to define high probability regions. The ParaSol method was applied to a simple bucket model and to a Soil and Water Assessment Tool (SWAT) model Of Honey Creek, OH, USA. The bucket model case showed the success of the method in finding the minimum and the applicability of the statistics under importance sampling. Both cases showed that the confidence regions are very small when the chi(2) statistics are used and even smaller when using the Bayesian statistics. By comparing the ParaSol uncertainty results to those derived from 500,000 Monte Carlo simulations it was shown that the SCE-UA sampling used for ParaSol was more effective and efficient, as none of the Monte Carlo samples were close to the minimum or even within the confidence region defined by ParaSol.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
keyword
HYDROLOGIC-MODELS, PARAMETER-ESTIMATION, OPTIMIZATION, SENSITIVITY, MULTIPLE, auto-calibration, model, river basin, AUTOMATIC CALIBRATION, RAINFALL-RUNOFF MODELS, water quality
journal title
JOURNAL OF HYDROINFORMATICS
J. Hydroinform.
volume
9
issue
4
pages
277 - 291
Web of Science type
Article
Web of Science id
000249817100003
JCR category
ENGINEERING, CIVIL
JCR impact factor
0.457 (2007)
JCR rank
50/87 (2007)
JCR quartile
3 (2007)
ISSN
1464-7141
DOI
10.2166/hydro.2007.104
language
English
UGent publication?
yes
classification
A1
id
743943
handle
http://hdl.handle.net/1854/LU-743943
date created
2009-09-09 09:01:00
date last changed
2009-10-23 15:33:06
@article{743943,
  abstract     = {Catchment water quality models have many parameters, several output variables and a complex structure leading to multiple minima in the objective function. General uncertainty/optimization methods based on random sampling (e.g. GLUE) or local methods (e.g. PEST) are often not applicable for theoretical or practical reasons. This paper presents {\textacutedbl}ParaSol{\textacutedbl}, a method that performs optimization and uncertainty analysis for complex models such as distributed water quality models. Optimization is done by adapting the Shuffled Complex Evolution algorithm (SCE-UA) to account for multi-objective problems and for large numbers of parameters. The simulations performed by the SCE-UA are used further for uncertainty analysis and thereby focus the uncertainty analysis on solutions near the optimum/optima. Two methods have been developed that select {\textacutedbl}good{\textacutedbl} results out of these simulations based on an objective threshold. The first method is based on chi(2) statistics to delineate the confidence regions around the optimum/optima and the second method uses Bayesian statistics to define high probability regions. The ParaSol method was applied to a simple bucket model and to a Soil and Water Assessment Tool (SWAT) model Of Honey Creek, OH, USA. The bucket model case showed the success of the method in finding the minimum and the applicability of the statistics under importance sampling. Both cases showed that the confidence regions are very small when the chi(2) statistics are used and even smaller when using the Bayesian statistics. By comparing the ParaSol uncertainty results to those derived from 500,000 Monte Carlo simulations it was shown that the SCE-UA sampling used for ParaSol was more effective and efficient, as none of the Monte Carlo samples were close to the minimum or even within the confidence region defined by ParaSol.},
  author       = {van Griensven, A. and Meixner, T.},
  issn         = {1464-7141},
  journal      = {JOURNAL OF HYDROINFORMATICS},
  keyword      = {HYDROLOGIC-MODELS,PARAMETER-ESTIMATION,OPTIMIZATION,SENSITIVITY,MULTIPLE,auto-calibration,model,river basin,AUTOMATIC CALIBRATION,RAINFALL-RUNOFF MODELS,water quality},
  language     = {eng},
  number       = {4},
  pages        = {277--291},
  title        = {A global and efficient multi-objective auto-calibration and uncertainty estimation method for water quality catchment models},
  url          = {http://dx.doi.org/10.2166/hydro.2007.104},
  volume       = {9},
  year         = {2007},
}

Chicago
van Griensven, A., and T. Meixner. 2007. “A Global and Efficient Multi-objective Auto-calibration and Uncertainty Estimation Method for Water Quality Catchment Models.” Journal of Hydroinformatics 9 (4): 277–291.
APA
van Griensven, A., & Meixner, T. (2007). A global and efficient multi-objective auto-calibration and uncertainty estimation method for water quality catchment models. JOURNAL OF HYDROINFORMATICS, 9(4), 277–291.
Vancouver
1.
van Griensven A, Meixner T. A global and efficient multi-objective auto-calibration and uncertainty estimation method for water quality catchment models. JOURNAL OF HYDROINFORMATICS. 2007;9(4):277–91.
MLA
van Griensven, A., and T. Meixner. “A Global and Efficient Multi-objective Auto-calibration and Uncertainty Estimation Method for Water Quality Catchment Models.” JOURNAL OF HYDROINFORMATICS 9.4 (2007): 277–291. Print.