Improved sea level anomaly prediction through combination of data relationship analysis and genetic programming in Singapore Regional Waters

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Kurniawan:2014:CG,
  author =       "Alamsyah Kurniawan and Seng Keat Ooi and 
                 Vladan Babovic",
  title =        "Improved sea level anomaly prediction through
                 combination of data relationship analysis and genetic
                 programming in Singapore Regional Waters",
  journal =      "Computer \& Geosciences",
  volume =       "72",
  pages =        "94--104",
  year =         "2014",
  ISSN =         "0098-3004",
  DOI =          "doi:10.1016/j.cageo.2014.07.007",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0098300414001642",
  abstract =     "With recent advances in measurement and information
                 technology, there is an abundance of data available for
                 analysis and modelling of hydrodynamic systems. Spatial
                 and temporal data coverage, better quality and
                 reliability of data modelling and data driven
                 techniques have resulted in more favourable acceptance
                 by the hydrodynamic community. The data mining tools
                 and techniques are being applied in variety of
                 hydro-informatics applications ranging from data mining
                 for pattern discovery to data driven models and
                 numerical model error correction. The present study
                 explores the feasibility of applying mutual information
                 theory by evaluating the amount of information
                 contained in observed and prediction errors of
                 non-tidal barotropic numerical modelling (i.e. assuming
                 that the hydrodynamic model, available at this point,
                 is best representation of the physics in the domain of
                 interest) by relating them to variables that reflect
                 the state at which the predictions are made such as
                 input data, state variables and model output. In
                 addition, the present study explores the possibility of
                 employing `genetic programming' (GP) as an offline data
                 driven modelling tool to capture the sea level anomaly
                 (SLA) dynamics and then using them for updating the
                 numerical model prediction in real time applications.
                 These results suggest that combination of data
                 relationship analysis and GP models helps to improve
                 the forecasting ability by providing information of
                 significant predicative parameters. It is found that GP
                 based SLA prediction error forecast model can provide
                 significant improvement when applied as data
                 assimilation schemes for updating the SLA prediction
                 obtained from primary hydrodynamic models.",
  keywords =     "genetic algorithms, genetic programming, Data model
                 integration, Average mutual information, Error
                 forecasting, Tide-surge interaction",
}

Genetic Programming entries for Alamsyah Kurniawan Seng Keat Ooi Vladan Babovic

Citations