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    Intermittent stream flow forecasting and modelling with hybrid wavelet neuro-fuzzy model

    Access Status
    Fulltext not available
    Authors
    Badrzadeh, Honey
    Sarukkalige, Priyantha Ranjan
    Jayawardena, A.
    Date
    2018
    Type
    Journal Article
    
    Metadata
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    Citation
    Badrzadeh, H. and Sarukkalige, P.R. and Jayawardena, A. 2018. Intermittent stream flow forecasting and modelling with hybrid wavelet neuro-fuzzy model. Hydrology Research. 49 (1): pp. 27-40.
    Source Title
    Hydrology Research
    DOI
    10.2166/nh.2017.163
    ISSN
    1998-9563
    School
    School of Civil and Mechanical Engineering (CME)
    URI
    http://hdl.handle.net/20.500.11937/67284
    Collection
    • Curtin Research Publications
    Abstract

    In this paper, an advanced stream flow forecasting model is developed by applying data-preprocessing techniques on adaptive neuro-fuzzy inference system (ANFIS). Wavelet multi-resolution analysis is coupled with an ANFIS model to develop a hybrid wavelet neuro-fuzzy (WNF) model. Different models with different input selection and structures are developed for daily, weekly and monthly stream flow forecasting in Railway Parade station on Ellen Brook River, Western Australia. The stream flow time series is decomposed into multi-frequency time series by discrete wavelet transform using the Haar, Coiflet and Daubechies mother wavelets. The wavelet coefficients are then imposed as input data to the neuro-fuzzy model. Models are developed based on Takagi-Sugeno-Kang fuzzy inference system with the grid partitioning approach for initializing the fuzzy rule-based structure. Mean-square error and Nash-Sutcliffe coefficient are chosen as the performance criteria. The results of the application show that the right selection of the inputs with high autocorrelation function improves the accuracy of forecasting. Comparing the performance of the hybrid WNF models with those of the original ANFIS models indicates that the hybrid WNF models produce significantly better results especially in longer-term forecasting.

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