River flow forecasting using an integrated approach of wavelet multi-resolution analysis and computational intelligence techniques
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 de...
| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
Curtin University
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/2210 |
| _version_ | 1848743890201346048 |
|---|---|
| author | Badrzadeh, Honey |
| author_facet | Badrzadeh, Honey |
| author_sort | Badrzadeh, Honey |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T05:52:45Z |
| format | Thesis |
| id | curtin-20.500.11937-2210 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T05:52:45Z |
| publishDate | 2014 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-22102017-02-20T06:37:29Z River flow forecasting using an integrated approach of wavelet multi-resolution analysis and computational intelligence techniques Badrzadeh, Honey 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. 2014 Thesis http://hdl.handle.net/20.500.11937/2210 en Curtin University fulltext |
| spellingShingle | Badrzadeh, Honey River flow forecasting using an integrated approach of wavelet multi-resolution analysis and computational intelligence techniques |
| title | River flow forecasting using an integrated approach of wavelet multi-resolution analysis and computational intelligence techniques |
| title_full | River flow forecasting using an integrated approach of wavelet multi-resolution analysis and computational intelligence techniques |
| title_fullStr | River flow forecasting using an integrated approach of wavelet multi-resolution analysis and computational intelligence techniques |
| title_full_unstemmed | River flow forecasting using an integrated approach of wavelet multi-resolution analysis and computational intelligence techniques |
| title_short | River flow forecasting using an integrated approach of wavelet multi-resolution analysis and computational intelligence techniques |
| title_sort | river flow forecasting using an integrated approach of wavelet multi-resolution analysis and computational intelligence techniques |
| url | http://hdl.handle.net/20.500.11937/2210 |