Curtin University Homepage
  • Library
  • Help
    • Admin

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    Using Artificial Neural Networks to estimate sea level in continental and island coastal environments

    Access Status
    Fulltext not available
    Authors
    Makarynskyy, Oleg
    Makarynska, D.
    Kuhn, Michael
    Featherstone, Will
    Date
    2005
    Type
    Book Chapter
    
    Metadata
    Show full item record
    Citation
    Makarynskyy, O and Makarynska, D and Kuhn, M and Featherstone, W E. 2005. Using Artificial Neural Networks to estimate sea level in continental and island coastal environments. In Cheng, L and Yeow, K (ed), Hydrodynamics IV: Theory and Applications, 451-457. London: Taylor & Francis Group.
    Source Title
    Hydrodynamics IV: Theory and Applications
    Faculty
    Western Australian Centre for Geodesy
    Division of Resources and Environment
    Department of Spatial Sciences
    Remarks

    ISBN for e-book version 0203020685

    URI
    http://hdl.handle.net/20.500.11937/27462
    Collection
    • Curtin Research Publications
    Abstract

    The knowledge of sea level variations is of great importance in geoenvironmental and ocean-engineering applications. Estimations of sea level change with different warning times are of vital importance for the population of low-lying regions and islands. This contribution describes some recent advances in the application of a meshless artificial intelligence technique (neural networks) to the tasks of sea level retrieval and forecast. This technique was employed because it has been proven to approximate the non-linear behaviour in a geophysical system. The data used were taken from several SEAFRAME stations, which provide records for the Australian Baseline Sea Level Monitoring Project. A feed-forward, three-layered, artificial neural network was implemented to retrieve and predict sea level variations with different lead times. This methodology demonstrated reliable results in terms of the correlation coefficient (0.82-0.96), root mean square error (about 10% of tidal range) and scatter index (0.1-0.2), when compared with actual observations.

    Related items

    Showing items related by title, author, creator and subject.

    • The integration of bioacoustic indicators and artificial fear cues for the strategic management of kangaroo herbivory following fire and mining
      Biedenweg, Tine Ann Kristin (2010)
      Western grey kangaroos (Macropus fuliginosus) have not previously been subject to tests for susceptibility to auditory based deterrents. This study presented a mob of western grey kangaroos with a series of treatments to ...
    • Using Artificial Neural Networks to estimate sea level in continental and island coastal environments
      Makarynskyy, Oleg; Makarynska, D.; Kuhn, Michael; Featherstone, Will (2004)
      The knowledge of sea level variations is of great importance in geoenvironmental and ocean-engineering applications. Estimations of sea level change with different warning times are of vital importance for the population ...
    • Different XAI for different HRI
      Sheh, Raymond (2017)
      Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Artificial Intelligence (AI) has become more widespread in critical decision making at all levels of robotics, ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorTitleSubjectDocument TypeThis CollectionIssue DateAuthorTitleSubjectDocument Type

    My Account

    Admin

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Follow Curtin

    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Copyright | Disclaimer | Privacy statement | Accessibility

    Curtin would like to pay respect to the Aboriginal and Torres Strait Islander members of our community by acknowledging the traditional owners of the land on which the Perth campus is located, the Whadjuk people of the Nyungar Nation; and on our Kalgoorlie campus, the Wongutha people of the North-Eastern Goldfields.