Sea water level forecasting using genetic programming and comparing the performance with Artificial Neural Networks

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

@Article{AliGhorbani2010620,
  author =       "Mohammad Ali Ghorbani and Rahman Khatibi and 
                 Ali Aytek and Oleg Makarynskyy and Jalal Shiri",
  title =        "Sea water level forecasting using genetic programming
                 and comparing the performance with Artificial Neural
                 Networks",
  journal =      "Computer \& Geosciences",
  volume =       "36",
  number =       "5",
  pages =        "620--627",
  year =         "2010",
  ISSN =         "0098-3004",
  DOI =          "doi:10.1016/j.cageo.2009.09.014",
  URL =          "http://www.sciencedirect.com/science/article/B6V7D-4YCS020-1/2/514d629e145e62f37dbf599a1a7608a9",
  keywords =     "genetic algorithms, genetic programming, Sea-level
                 variations, Forecasting, Artificial Neural Networks,
                 Comparative studies",
  abstract =     "Water level forecasting at various time intervals
                 using records of past time series is of importance in
                 water resources engineering and management. In the last
                 20 years, emerging approaches over the conventional
                 harmonic analysis techniques are based on using Genetic
                 Programming (GP) and Artificial Neural Networks (ANNs).
                 In the present study, the GP is used to forecast sea
                 level variations, three time steps ahead, for a set of
                 time intervals comprising 12 h, 24 h, 5 day and 10 day
                 time intervals using observed sea levels. The
                 measurements from a single tide gauge at Hillarys Boat
                 Harbour, Western Australia, were used to train and
                 validate the employed GP for the period from December
                 1991 to December 2002. Statistical parameters, namely,
                 the root mean square error, correlation coefficient and
                 scatter index, are used to measure their performances.
                 These were compared with a corresponding set of
                 published results using an Artificial Neural Network
                 model. The results show that both these artificial
                 intelligence methodologies perform satisfactorily and
                 may be considered as alternatives to the harmonic
                 analysis.",
}

Genetic Programming entries for Mohammad Ali Ghorbani Rahman Khatibi Ali Aytek Oleg Makarynskyy Jalal Shiri

Citations