Comparison between M5′ model tree and neural networks for prediction of significant wave height in Lake Superior

2009 ◽  
Vol 36 (15-16) ◽  
pp. 1175-1181 ◽  
Author(s):  
A. Etemad-Shahidi ◽  
J. Mahjoobi
Keyword(s):  
Neural Networks ◽  
Wave Height ◽  
Significant Wave Height ◽  
Lake Superior ◽  
M5 Model Tree ◽  
Significant Wave ◽  
Model Tree ◽  
M5 Model

Neural Networks Based Simulation of Significant Wave Height

2007 ◽  
Author(s):  
H. Bazargan ◽  
H. Bahai ◽  
A. Aminzadeh-Gohari ◽  
A. Bazargan
Keyword(s):  
Neural Networks ◽  
Wave Height ◽  
Significant Wave Height ◽  
Optimization Technique ◽  
Conditional Probability Density ◽  
Significant Wave ◽  
The Pacific ◽  
Wave Heights ◽  
Significant Wave Heights ◽  
Long Term Forecasting

A large number of ocean activities call for real time or on-line forecasting of wind wave characteristics including significant wave height (Hs). The work reported in this paper uses statistics, and artificial neural networks trained with an optimization technique called simulated annealing to estimate the parameters of a probability distribution called hepta-parameter spline for the conditional probability density functions (pdf’s) of significant wave heights given their eight immediate preceding 3-hourly observed Hs’s. These pdf’s are used in the simulation of significant wave heights related to a location in the Pacific. The paper also deals with short and long term forecasting of Hs for the region through generating random variates from the spline distribution.

scholarly journals Prediction of the wave power in the Black Sea based on wind speed using artificial neural networks

2018 ◽  
Vol 51 ◽  
pp. 01006
Author(s):  
Sorin Ciortan ◽  
Eugen Rusu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
Wave Height ◽  
Black Sea ◽  
Significant Wave Height ◽  
The Black Sea ◽  
Wave Power ◽  
Significant Wave ◽  
Artificial Neural

The paper proposes a prediction methodology for the significant wave height (and implicitly the wave power), based on the artificial neural networks. The proposed approach takes as input data the wind speed values recorded for different time periods. The prediction of significant wave height is useful both for assessment of wave energy as also for marine equipment design and navigation. The data used cover the time interval 1999 to 2007 and it was measured on Gloria drilling unit, which operates in the Romanian nearshore of the Black Sea at about 500 meters depth.

Significant wave height record extension by neural networks and reanalysis wind data

2015 ◽  
Vol 94 ◽  
pp. 128-140 ◽  
Author(s):  
D.J. Peres ◽  
C. Iuppa ◽  
L. Cavallaro ◽  
A. Cancelliere ◽  
E. Foti
Keyword(s):  
Neural Networks ◽  
Wave Height ◽  
Significant Wave Height ◽  
Wind Data ◽  
Significant Wave

Real-time significant wave height estimation from raw ocean images based on 2D and 3D deep neural networks

2020 ◽  
Vol 201 ◽  
pp. 107129 ◽  
Author(s):  
Heejeong Choi ◽  
Minsik Park ◽  
Gyubin Son ◽  
Jaeyun Jeong ◽  
Jaesun Park ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Wave Height ◽  
Significant Wave Height ◽  
Deep Neural Networks ◽  
Height Estimation ◽  
Significant Wave ◽  
2D And 3D
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