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

scholarly journals Studi Hindcasting Dalam Menentukan Karakteristik Gelombang dan Klasifikasi Zona Surf Di Pantai Uluwatu, Bali

2018 ◽  
Vol 5 (1) ◽  
pp. 119
Author(s):  
Karina Santoso ◽  
I Dewa Nyoman Nurweda Putra ◽  
I Gusti Bagus Sila Dharma
Keyword(s):  
Wave Height ◽  
Calculation Result ◽  
Significant Wave Height ◽  
Surf Zone ◽  
Wind Data ◽  
Wave Transformation ◽  
Breaking Wave ◽  
Significant Wave ◽  
The World

Bali is one of the islands where there are many surf zones with various characteristics. In addition, Bali is also a heaven with a classy wave for the surfers of the world. One of the most challenging places to surf in Bali is Uluwatu Beach. Uluwatu Beach is ranked the 3rd best surf spot in the world version of CNN Travel in 2012. Wind causes sea waves, therefore wind data can be used to estimate the height and direction of the waves. Wave Hindcasting with Sverdrup, Munk and Bretschneider (SMB) method is calculated based on wind data for 10 years (2001 - 2010) from BMKG Ngurah Rai Station - Denpasar to obtain a significant wave height and period. In this research, it is necessary to approach through Hindcasting procedure, wave transformation analysis and surfing Terminology in determining the type of breaking wave and classification of surf zone in Uluwatu Beach area. Wave calculation result in Uluwatu Beach dominated by wave that coming from west side with significant wave height (Hs) of 0.98 m and significant wave period (Ts) of 5.21 s. The wave height due to the influence of wave refraction and shoaling is 0.976 m. The breaking wave height obtained from the calculation is 1.04 m at a depth of 0.849 m. From the result in this research, it can be concluded that the breaking wave type that occurred at Uluwatu Beach is plunging type according to the calculation result from its Irribaren number (0.4 <Ni <2.3). The classification of the surf zone at Uluwatu Beach based on its breakup type of wave is thought to be a good zone for surfers on intermediate level.

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.

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

scholarly journals Application of neural networks and support vector machine for significant wave height prediction

Oceanologia ◽  
2017 ◽  
Vol 59 (3) ◽  
pp. 331-349 ◽  
Author(s):  
Jadran Berbić ◽  
Eva Ocvirk ◽  
Dalibor Carević ◽  
Goran Lončar
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Wave Height ◽  
Significant Wave Height ◽  
Support Vector ◽  
Significant Wave ◽  
Height Prediction

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.

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