Wave Height Quantification Using Land Based Seismic Data with Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Donne:2014:CEC,
  title =        "Wave Height Quantification Using Land Based Seismic
                 Data with Grammatical Evolution",
  author =       "Sarah Donne and Miguel Nicolau and 
                 Christopher Bean and Michael O'Neill",
  pages =        "2909--2916",
  booktitle =    "Proceedings of the 2014 IEEE Congress on Evolutionary
                 Computation",
  year =         "2014",
  month =        "6-11 " # jul,
  editor =       "Carlos A. {Coello Coello}",
  address =      "Beijing, China",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 Evolution, Real-world applications",
  DOI =          "doi:10.1109/CEC.2014.6900563",
  abstract =     "Accurate, real time, continuous ocean wave height
                 measurements are required for the initialisation of
                 ocean wave forecast models, model hindcasting, and
                 climate studies. These measurements are usually
                 obtained using in situ ocean buoys or by satellite
                 altimetry, but are sometimes incomplete due to
                 instrument failure or routine network upgrades. In such
                 situations, a reliable gap filling technique is
                 desirable to provide a continuous and accurate ocean
                 wave field record. Recorded on a land based seismic
                 network are continuous seismic signals known as
                 microseisms. These microseisms are generated by the
                 interactions of ocean waves and will be used in the
                 estimation of ocean wave heights. Grammatical Evolution
                 is applied in this study to generate symbolic models
                 that best estimate ocean wave height from terrestrial
                 seismic data, and the best model is validated against
                 an Artificial Neural Network. Both models are tested
                 over a five month period of 2013, and an analysis of
                 the results obtained indicates that the approach is
                 robust and that it is possible to estimate ocean wave
                 heights from land based seismic data.",
  notes =        "WCCI2014",
}

Genetic Programming entries for Sarah Donne Miguel Nicolau Christopher Bean Michael O'Neill

Citations