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dc.contributor.authorBadrzadeh, Honey
dc.contributor.supervisorDr Ranjan Sarukkalige
dc.contributor.supervisorProf. Amithirigala Jayawardena
dc.date.accessioned2017-01-30T10:18:45Z
dc.date.available2017-01-30T10:18:45Z
dc.date.created2015-02-25T01:13:17Z
dc.date.issued2014
dc.identifier.urihttp://hdl.handle.net/20.500.11937/2210
dc.description.abstract

In this research an attempt is made to develop highly accurate river flow forecasting models. Wavelet multi-resolution analysis is applied in conjunction with artificial neural networks and adaptive neuro-fuzzy inference system. Various types and structure of computational intelligence models are developed and applied on four different rivers in Australia. Research outcomes indicate that forecasting reliability is significantly improved by applying proposed hybrid models, especially for longer lead time and peak values.

dc.languageen
dc.publisherCurtin University
dc.titleRiver flow forecasting using an integrated approach of wavelet multi-resolution analysis and computational intelligence techniques
dc.typeThesis
dcterms.educationLevelPh.D.
curtin.departmentDepartment of Civil Engineering
curtin.accessStatusOpen access


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