Prediction of sea water levels using wind information and soft computing techniques
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- @Article{Nitsure:2014:AOR,
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author = "S. P. Nitsure and S. N. Londhe and K. C. Khare",
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title = "Prediction of sea water levels using wind information
and soft computing techniques",
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journal = "Applied Ocean Research",
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volume = "47",
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pages = "344--351",
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year = "2014",
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keywords = "genetic algorithms, genetic programming, ANN, Sea
water levels, Sea level anomaly, Wind shear velocity,
Artificial Neural Network",
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ISSN = "0141-1187",
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URL = "http://www.sciencedirect.com/science/article/pii/S0141118714000613",
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DOI = "doi:10.1016/j.apor.2014.07.003",
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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
satisfactory.",
- }
Genetic Programming entries for
S P Nitsure
S N Londhe
K C Khare
Citations