Combined Wavelet-Neural Network Model for Intermittent Stream Flow Prediction
Achieving accurate intermittent river flow forecasting, plays a key role in water resources and environmental management. Water demands are increasing while surface water availability is likely to decrease in Western Australia. Understandably, reliable information on current and future water availab...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
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Research Publishing Services
2012
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/28437 |
| _version_ | 1848752536626921472 |
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| author | Badrzadeh, Honey Sarukkalige, Priyantha Jayawardena, A. |
| author2 | Vanissom Vimonsatit |
| author_facet | Vanissom Vimonsatit Badrzadeh, Honey Sarukkalige, Priyantha Jayawardena, A. |
| author_sort | Badrzadeh, Honey |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Achieving accurate intermittent river flow forecasting, plays a key role in water resources and environmental management. Water demands are increasing while surface water availability is likely to decrease in Western Australia. Understandably, reliable information on current and future water availability is essential for properly manage and share the limited water resources. Forecasting intermittent stream flow is quite limited due to the complexity of fitting models to their time series as they do not have flow for some intervals. In this paper Wavelet-Neural Networks (WNN) technique is studied to reach accurate and reliable daily river flow prediction. WNN is based on combination of wavelet analysis and Artificial Neural Network (ANN), which is one of the most reliable recent hybrid methods for non-stationary hydrological time series predictions. Daily stream flow and precipitation historical data from Northam weir station on Avon River, Western Australia are used in this study. The observed stream flow and rainfall time series are both decomposed by Daubechies4 and Coiflet1 Wavelet transforms. Then the sub-series are added up to develop new time series for imposing as input data to the multilayer perceptron neural networks (MLP). Comparing the results of different wavelet neural networks with those of the single ANNs model indicates that preprocessing data with discrete wavelet transform have significantly improved artificial neural in terms of selected performance criteria. |
| first_indexed | 2025-11-14T08:10:11Z |
| format | Conference Paper |
| id | curtin-20.500.11937-28437 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:10:11Z |
| publishDate | 2012 |
| publisher | Research Publishing Services |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-284372023-02-07T08:01:18Z Combined Wavelet-Neural Network Model for Intermittent Stream Flow Prediction Badrzadeh, Honey Sarukkalige, Priyantha Jayawardena, A. Vanissom Vimonsatit Amarjit Singh Siamak Yazdani Wavelet transform Time series Artificial neural network Forecasting Non-stationary Stream flow Achieving accurate intermittent river flow forecasting, plays a key role in water resources and environmental management. Water demands are increasing while surface water availability is likely to decrease in Western Australia. Understandably, reliable information on current and future water availability is essential for properly manage and share the limited water resources. Forecasting intermittent stream flow is quite limited due to the complexity of fitting models to their time series as they do not have flow for some intervals. In this paper Wavelet-Neural Networks (WNN) technique is studied to reach accurate and reliable daily river flow prediction. WNN is based on combination of wavelet analysis and Artificial Neural Network (ANN), which is one of the most reliable recent hybrid methods for non-stationary hydrological time series predictions. Daily stream flow and precipitation historical data from Northam weir station on Avon River, Western Australia are used in this study. The observed stream flow and rainfall time series are both decomposed by Daubechies4 and Coiflet1 Wavelet transforms. Then the sub-series are added up to develop new time series for imposing as input data to the multilayer perceptron neural networks (MLP). Comparing the results of different wavelet neural networks with those of the single ANNs model indicates that preprocessing data with discrete wavelet transform have significantly improved artificial neural in terms of selected performance criteria. 2012 Conference Paper http://hdl.handle.net/20.500.11937/28437 Research Publishing Services restricted |
| spellingShingle | Wavelet transform Time series Artificial neural network Forecasting Non-stationary Stream flow Badrzadeh, Honey Sarukkalige, Priyantha Jayawardena, A. Combined Wavelet-Neural Network Model for Intermittent Stream Flow Prediction |
| title | Combined Wavelet-Neural Network Model for Intermittent Stream Flow Prediction |
| title_full | Combined Wavelet-Neural Network Model for Intermittent Stream Flow Prediction |
| title_fullStr | Combined Wavelet-Neural Network Model for Intermittent Stream Flow Prediction |
| title_full_unstemmed | Combined Wavelet-Neural Network Model for Intermittent Stream Flow Prediction |
| title_short | Combined Wavelet-Neural Network Model for Intermittent Stream Flow Prediction |
| title_sort | combined wavelet-neural network model for intermittent stream flow prediction |
| topic | Wavelet transform Time series Artificial neural network Forecasting Non-stationary Stream flow |
| url | http://hdl.handle.net/20.500.11937/28437 |