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    Hourly runoff forecasting for flood risk management: Application of various computational intelligence models

    Access Status
    Fulltext not available
    Authors
    Badrzadeh, H.
    Sarukkalige, Priyantha Ranjan
    Jayawardena, A.
    Date
    2015
    Type
    Journal Article
    
    Metadata
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    Citation
    Badrzadeh, H. and Sarukkalige, P.R. and Jayawardena, A. 2015. Hourly runoff forecasting for flood risk management: Application of various computational intelligence models. Journal of Hydrology. 529 (3): pp. 1633-1643.
    Source Title
    Journal of Hydrology
    DOI
    10.1016/j.jhydrol.2015.07.057
    ISSN
    0022-1694
    School
    Department of Civil Engineering
    URI
    http://hdl.handle.net/20.500.11937/14053
    Collection
    • Curtin Research Publications
    Abstract

    © 2015 Elsevier B.V. Reliable river flow forecasts play a key role in flood risk mitigation. Among different approaches of river flow forecasting, data driven approaches have become increasingly popular in recent years due to their minimum information requirements and ability to simulate nonlinear and non-stationary characteristics of hydrological processes. In this study, attempts are made to apply four different types of data driven approaches, namely traditional artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), wavelet neural networks (WNN), and, hybrid ANFIS with multi resolution analysis using wavelets (WNF). Developed models applied for real time flood forecasting at Casino station on Richmond River, Australia which is highly prone to flooding. Hourly rainfall and runoff data were used to drive the models which have been used for forecasting with 1, 6, 12, 24, 36 and 48. h lead-time. The performance of models further improved by adding an upstream river flow data (Wiangaree station), as another effective input. All models perform satisfactorily up to 12. h lead-time. However, the hybrid wavelet-based models significantly outperforming the ANFIS and ANN models in the longer lead-time forecasting. The results confirm the robustness of the proposed structure of the hybrid models for real time runoff forecasting in the study area.

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