Prediction of sea water levels using wind information and soft computing techniques

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "S. P. Nitsure and S. N. Londhe and K. C. Khare",
  title =        "Prediction of sea water levels using wind information
                 and soft computing techniques",
  journal =      "Applied Ocean Research",
  volume =       "47",
  pages =        "344--351",
  year =         "2014",
  ISSN =         "0141-1187",
  DOI =          "doi:10.1016/j.apor.2014.07.003",
  URL =          "",
  abstract =     "Large variations of sea water levels are a matter of
                 concern for the offshore and coastal locations having
                 shallow water depths. Safety of maritime activities,
                 and properties, as well as human lives at such
                 locations can be ensured by using the accurately
                 predicted water levels. Harmonic analysis is
                 traditionally employed for tide predictions, but often
                 the values of predicted tides and observed (measured)
                 water levels are not identical. The difference between
                 them is called sea level anomaly. This can be
                 attributed to non-inclusion of meteorological
                 parameters as an input for tide prediction. Therefore
                 other prediction techniques become necessary. The
                 earlier studies on sea level predictions indicate
                 better efficiency of alternate techniques such as
                 Artificial Neural Network (ANN) and Genetic Programming
                 (GP), and that most researchers have used sea level
                 time series as model inputs. Present work predicts sea
                 levels indirectly by predicting sea level anomalies
                 (SLAs) using hourly local wind shear velocity
                 components of the present time and up to the previous
                 12 h as inputs at four stations near the USA coastline
                 with the techniques of GP and ANN. The error measures
                 and graphs indicate that predictions are
  keywords =     "genetic algorithms, genetic programming, Sea water
                 levels, Sea level anomaly, Wind shear velocity,
                 Artificial Neural Network",

Genetic Programming entries for S P Nitsure S N Londhe K C Khare