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dc.contributor.authorJahandideh-Tehrani, Mahsa
dc.contributor.authorJenkins, Graham
dc.contributor.authorHelfer, Fernanda
dc.date.accessioned2020-10-19T01:42:13Z
dc.date.available2020-10-19T01:42:13Z
dc.date.issued2020
dc.identifier.issn1389-4420
dc.identifier.doi10.1007/s11081-020-09538-3
dc.identifier.urihttp://hdl.handle.net/10072/398443
dc.description.abstractReal-time and short-term prediction of river flow is essential for efficient flood management. To obtain accurate flow predictions, a reliable rainfall-runoff model must be used. This study proposes the application of two evolutionary algorithms, particle swarm optimization (PSO) and genetic algorithm (GA), to train the artificial neural network (ANN) parameters in order to overcome the ANN drawbacks, such as slow learning speed and frequent trapping at local optimum. These hybrid ANN-PSO and ANN-GA approaches were validated to equip natural hazard decision makers with a robust tool for forecasting real-time streamflow as a function of combinations of different lagged rainfall and streamflow in a small catchment in Southeast Queensland, Australia. Different input combinations of lagged rainfall and streamflow (delays of one, two and three days) were tested to investigate the sensitivity of the model to the number of delayed days, and to identify the effective model input combinations for the accurate prediction of real-time streamflow, which has not yet been recognized in other studies. The results indicated that the ANN-PSO model significantly outperformed the ANN-GA model in terms of convergence speed, accuracy, and fitness function evaluation. Additionally, it was found that the rainfall and streamflow with 3-day lag time had less impact on the predicted streamflow of the studied basin, confirming that the flow of the studied river is significantly correlated with only 2-day lagged rainfall and streamflow.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofjournalOptimization and Engineering
dc.subject.fieldofresearchMathematical Sciences
dc.subject.fieldofresearchEngineering
dc.subject.fieldofresearchcode01
dc.subject.fieldofresearchcode09
dc.subject.keywordsScience & Technology
dc.subject.keywordsPhysical Sciences
dc.subject.keywordsEngineering, Multidisciplinary
dc.subject.keywordsOperations Research & Management Science
dc.titleA comparison of particle swarm optimization and genetic algorithm for daily rainfall-runoff modelling: a case study for Southeast Queensland, Australia
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationJahandideh-Tehrani, M; Jenkins, G; Helfer, F, A comparison of particle swarm optimization and genetic algorithm for daily rainfall-runoff modelling: a case study for Southeast Queensland, Australia, Optimization and Engineering, 2020
dc.date.updated2020-10-16T05:44:03Z
dc.description.versionPost-print
gro.description.notepublicThis publication has been entered in Griffith Research Online as an advanced online version.
gro.rights.copyright© 2020 Springer-Verlag. This is an electronic version of an article published in Optimization and Engineering, 2020. Acta Informatica is available online at: http://link.springer.com/ with the open URL of your article.
gro.hasfulltextFull Text
gro.griffith.authorJahandideh-Tehrani, Mahsa
gro.griffith.authorHelfer, Fernanda
gro.griffith.authorJenkins, Graham A.


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