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    Combined Wavelet-Neural Network Model for Intermittent Stream Flow Prediction

    Access Status
    Fulltext not available
    Authors
    Badrzadeh, Honey
    Sarukkalige, Priyantha
    Jayawardena, A.
    Date
    2012
    Type
    Conference Paper
    
    Metadata
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    Citation
    Badrzadeh, Honey and Sarukkalige, Ranjan and Jayawardena, A. W. 2012. Combined Wavelet-Neural Network Model for Intermittent Stream Flow Prediction, in Vimonsatit, V. and Singh, A. and Yazdani, S. (ed), Research, Development, and Practice in Structural Engineering and Construction, The 1st Australasia and South East Asia Conference in Structural Engineering and Construction (ASEA-SEC-1), Nov 28-Dec 2 2012, pp. 769-774. Perth, Western Australia: Research Publishing Services.
    Source Title
    Proceedings of the 1st Australasia and South East Asia Conference in Structural Engineering and Construction (ASEA-SEC-1)
    Source Conference
    The 1st Australasia and South East Asia Conference in Structural Engineering and Construction (ASEA-SEC-1)
    ISBN
    978-981-07-3678-1
    URI
    http://hdl.handle.net/20.500.11937/28437
    Collection
    • Curtin Research Publications
    Abstract

    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.

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