Improved Sea Level Anomaly Prediction Through Genetic Programming In Singapore Regional Waters

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Alamsyah Kurniawan and Seng Keat Ooi and 
                 Vladan Babovic",
  title =        "Improved Sea Level Anomaly Prediction Through Genetic
                 Programming In Singapore Regional Waters",
  booktitle =    "11th International Conference on Hydroinformatics",
  year =         "2014",
  address =      "New York, USA",
  month =        aug # " 17-21",
  organisation = "IAHR/IWA Joint Committee on Hydroinformatics",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-0-692-28129-1",
  URL =          "",
  size =         "8 pages",
  abstract =     "With the recent advances in measurement and
                 information technology, there is an abundance of data
                 available for analysis and modelling of hydrodynamic
                 systems. With increasing spatial and temporal data
                 coverage, better quality and reliability of data
                 modelling and data driven techniques are becoming more
                 favourable and acceptable to the hydrodynamic
                 community. The data model integration tools and
                 techniques are being applied in variety of
                 hydroinformatics applications ranging from simple data
                 mining for pattern discovery to data driven models and
                 numerical model error correction. The present study
                 explores the possibility of employing genetic
                 programming (GP) as an offline data driven modelling
                 tool to capture the sea level anomalies (SLA) dynamics
                 in Singapore Regional Waters (SRW) and then using them
                 for updating the numerical model prediction in real
                 time applications. In the final stage 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. The results
                 have shown a good performance of non-tidal barotropic
                 numerical modelling and GP error forecast model to
                 forecast the SLA at Singapore Strait",
  notes =        "",

Genetic Programming entries for Alamsyah Kurniawan Seng Keat Ooi Vladan Babovic