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    Predicting sea level variations with artificial neural networks at Hillarys Boat Harbour, Western Australia

    19557_downloaded_stream_75.pdf (327.9Kb)
    Access Status
    Open access
    Authors
    Makarynskyy, Oleg
    Makarynska, D.
    Kuhn, Michael
    Featherstone, Will
    Date
    2004
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Makarynskyy, Oleg and Makarynska, Dina and Kuhn, Michael and Featherstone, Will. 2004. Predicting sea level variations with artificial neural networks at Hillarys Boat Harbour, Western Australia. Estuarine, Coastal and Shelf Science 61 (2): 351-360.
    Source Title
    Estuarine, Coastal and Shelf Science
    DOI
    10.1016/j.ecss.2004.06.004
    Faculty
    Division of Resources and Environment
    Department of Spatial Sciences
    Remarks

    O. Makarynskyy, , D. Makarynska, M. Kuhn and W.E. Featherstone(2004) Predicting sea level variations with artificial neural networks at Hillarys Boat Harbour, Western Australia. Estuarine, Coastal and Shelf Science 61(2):351-360.

    The link to this article is:

    http://dx.doi.org/10.1016/j.ecss.2004.06.004

    Copyright 2004 Elsevier B.V. All rights reserved

    URI
    http://hdl.handle.net/20.500.11937/43320
    Collection
    • Curtin Research Publications
    Abstract

    In the present study, the artificial intelligence meshless methodology of neural networks was used to predict hourly sea level variations for the following 24 hours, as well as for half-daily, daily, 5-daily and 10-daily mean sea levels. The methodology is site specific; therefore, as an example, the measurements from a single tide gauge at Hillarys Boat Harbour, Western Australia, for the period December 1991-December 2002 were used to train and to validate the employed neural networks. The results obtained show the feasibility of the neural sea level forecasts in terms of the correlation coefficient (0.7-0.9), root mean square error (about 10% of tidal range) and scatter index (0.1-0.2).

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