Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/17620
DC FieldValueLanguage
dc.contributorDepartment of Civil and Environmental Engineering-
dc.creatorXu, DM-
dc.creatorWang, WC-
dc.creatorChau, KW-
dc.creatorCheng, CT-
dc.creatorChen, SY-
dc.date.accessioned2015-05-26T08:12:56Z-
dc.date.available2015-05-26T08:12:56Z-
dc.identifier.issn1464-7141-
dc.identifier.urihttp://hdl.handle.net/10397/17620-
dc.language.isoenen_US
dc.publisherInternational Water Association Publishingen_US
dc.subjectGenetic algorithmen_US
dc.subjectGlobal optimization methodsen_US
dc.subjectMetropolis algorithmen_US
dc.subjectShuffled complex evolutionen_US
dc.subjectTime stepen_US
dc.subjectXinanjiang model calibrationen_US
dc.titleComparison of three global optimization algorithms for calibration of the Xinanjiang model parametersen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage174-
dc.identifier.epage193-
dc.identifier.volume15-
dc.identifier.issue1-
dc.identifier.doi10.2166/hydro.2012.053-
dcterms.abstractThe Xinanjiang model, a conceptual rainfall-runoff (CRR) model with distributed parameters, has been successfully and widely applied to flood forecasting of large basins in humid and semi-humid regions of China. With an increasing demand for timely and accurate forecasts in hydrology, how to obtain more appropriate parameters for CRR models has long been an important topic. These models have a large number of parameters which cannot be directly obtained from measurable quantities of catchments characteristics. In this study, three different optimization methods are used to calibrate the Xinanjiang streamflow model: genetic algorithm (GA), shuffled complex evolution of the University of Arizona (SCE-UA) and the recently developed shuffled complex evolution Metropolis algorithm of the University of Arizona (SCEM-UA), using streamflow data of the Shuangpai Reservoir in China. Two different time steps of 1 and 3 hr are used in the analysis. The results indicate that the SCEM-UA algorithm can infer the most probable parameter set and furnish useful information about the nature of the response surface in the vicinity of the optimum. Moreover, there is larger uncertainty for 1 hr forecasting than for 3 hr forecasting. This is significant in assessing risks in likely applications of Xinanjiang models.-
dcterms.bibliographicCitationJournal of hydroinformatics, 2013, v. 15, no. 1, p. 174-193-
dcterms.isPartOfJournal of hydroinformatics-
dcterms.issued2013-
dc.identifier.isiWOS:000312678700012-
dc.identifier.scopus2-s2.0-84873596192-
dc.identifier.rosgroupidr71520-
dc.description.ros2013-2014 > Academic research: refereed > Publication in refereed journal-
Appears in Collections:Journal/Magazine Article
Access
View full-text via PolyU eLinks SFX Query
Show simple item record

SCOPUSTM   
Citations

20
Last Week
0
Last month
0
Citations as of Sep 8, 2020

WEB OF SCIENCETM
Citations

23
Last Week
0
Last month
0
Citations as of Oct 20, 2020

Page view(s)

169
Last Week
6
Last month
Citations as of Oct 19, 2020

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.