Mahmoodi, Kumars; Ghassemi, Hassan; Nowruzi, Hashem
(Scientific Journals Maritime University of Szczecin, Zeszyty Naukowe Akademia Morska w Szczecinie,
)
Ocean wave energy is known as a renewable energy resource with high power potential and without negative
environmental impacts. Wave energy has a direct relationship with the ocean’s meteorological parameters. The
aim of the current study is to investigate the dependency between ocean wave energy flux and meteorological
parameters by using data mining methods (DMMs). For this purpose, a feed-forward neural network (FFNN),
a cascade-forward neural network (CFNN), and gene expression programming (GEP) are implemented as different
DMMs. The modeling is based on historical meteorological and wave data taken from the National Data
Buoy Center (NDBC). In all models, wind speed, air temperature, and sea temperature are input parameters.
In addition, the output is the wave energy flux which is obtained from the classical wave energy flux equation.
It is notable that, initially, outliers in the data sets were removed by the local distribution based outlier detector
(LDBOD) method to obtain the best and most accurate results. To evaluate the performance and accuracy of
the proposed models, two statistical measures, root mean square error (RMSE) and regression coefficient (R),
were used. From the results obtained, it was found that, in general, the FFNN and CFNN models gave a more
accurate prediction of wave energy from meteorological parameters in the absence of wave records than the
GEP method.