Parameter Uncertainty And Interactions In A Complex Catchment Modelling System

Fang, T; Ball, JE

Parameter Uncertainty And Interactions In A Complex Catchment Modelling System

Fang, T Ball, JE

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Publisher:: Chemical Industry Press
Publication Type:: Conference Proceeding
Citation:: Proceedings of the 9th International Conference on Hydroinformatics 2010, 2010, pp. 1359 - 1366
Issue Date:: 2010-01

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Field	Value	Language
dc.contributor.author	Fang, T	en_US
dc.contributor.author	Ball, JE https://orcid.org/0000-0001-8342-1916	en_US
dc.contributor.editor	N/A	en_US
dc.date	2010-09-07	en_US
dc.date.issued	2010-01	en_US
dc.identifier.citation	Proceedings of the 9th International Conference on Hydroinformatics 2010, 2010, pp. 1359 - 1366	en_US
dc.identifier.isbn	978-7-89472-324-6	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16398
dc.description.abstract	The importance of estimating parameter uncertainty has been recognised in the literature with Bayesian methods being the most popular approaches for undertaking parameter uncertainty estimation. With these approaches, the parameter uncertainty is described by the posterior distribution. Application of these methods usually requires a priori knowledge about the proposal distribution of the values of the parameters. However, usually there is little a priori knowledge about the proposal distribution for the parameter values in many catchment models. Furthermore, the difficulties of exploring posterior distribution of parameter values increases as the dimensionality of the problem increases. An alternative approach is to use a GLUE methodology to develop a heuristic estimate of the parameter uncertainty. In this application of a GLUE framework, a real-value coding genetic algorithm was used to undertake an uncertainty analysis on spatially variable control parameters associated with implementation of the Storm Water Management Model (SWMM). The focus of the study was to investigate the values of spatially variable control parameters that are associated with behavioural parameter sets in a complex catchment modelling system. Of particular concern was the frequency that values within a defined range occurred. Interactions among the values of the spatially variable parameters were investigated also. Results obtained from an application of the Pearson Correlation method indicated no clear relationship between any two control parameter values. However, distinct relationships among a group of parameters were detected suggesting that the simulation performance was affected more by the combination of parameter values than by the values of the individual parameters.	en_US
dc.publisher	Chemical Industry Press	en_US
dc.relation.ispartof	Proceedings of the 9th International Conference on Hydroinformatics 2010	en_US
dc.relation.ispartof	International Conference on Hydroinformatics	en_US
dc.title	Parameter Uncertainty And Interactions In A Complex Catchment Modelling System	en_US
dc.type	Conference Proceeding
utslib.location	China	en_US
utslib.location.activity	Tianjin, CHINA	en_US
utslib.for	090410 Water Treatment Processes	en_US
dc.location.activity	Tianjin, CHINA	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CTWW - Centre for Technology in Water and Wastewater Treatment
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.place-of-publication	China	en_US
pubs.start-date	2010-09-07	en_US

Abstract:

The importance of estimating parameter uncertainty has been recognised in the literature with Bayesian methods being the most popular approaches for undertaking parameter uncertainty estimation. With these approaches, the parameter uncertainty is described by the posterior distribution. Application of these methods usually requires a priori knowledge about the proposal distribution of the values of the parameters. However, usually there is little a priori knowledge about the proposal distribution for the parameter values in many catchment models. Furthermore, the difficulties of exploring posterior distribution of parameter values increases as the dimensionality of the problem increases. An alternative approach is to use a GLUE methodology to develop a heuristic estimate of the parameter uncertainty. In this application of a GLUE framework, a real-value coding genetic algorithm was used to undertake an uncertainty analysis on spatially variable control parameters associated with implementation of the Storm Water Management Model (SWMM). The focus of the study was to investigate the values of spatially variable control parameters that are associated with behavioural parameter sets in a complex catchment modelling system. Of particular concern was the frequency that values within a defined range occurred. Interactions among the values of the spatially variable parameters were investigated also. Results obtained from an application of the Pearson Correlation method indicated no clear relationship between any two control parameter values. However, distinct relationships among a group of parameters were detected suggesting that the simulation performance was affected more by the combination of parameter values than by the values of the individual parameters.

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http://hdl.handle.net/10453/16398