scholarly journals Prediction of Significant Wave Height in Korea Strait Using Machine Learning

2021 ◽  
Author(s):  
Sung Boo Park ◽  
Seong Yun Shin ◽  
Kwang Hyo Jung ◽  
Byung Gook Lee
Keyword(s):  
Machine Learning ◽  
Wave Height ◽  
Significant Wave Height ◽  
Significant Wave ◽  
Korea Strait

scholarly journals Prediction of dynamic mooring responses of a floating wind turbine using an artificial neural network

2021 ◽  
Vol 1201 (1) ◽  
pp. 012023
Author(s):  
F A Bjørni ◽  
S Lien ◽  
T Aa Midtgarden ◽  
G Kulia ◽  
A Verma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Wind Turbine ◽  
Wave Height ◽  
Significant Wave Height ◽  
Learning Methods ◽  
Mooring Lines ◽  
Peak Period ◽  
Significant Wave ◽  
Machine Learning Methods ◽  
Artificial Neural

Abstract Numerical simulations in coupled aero-hydro-servo-elastic codes are known to be a challenge for design and analysis of offshore wind turbine systems because of the large number of design load cases involved in checking the ultimate and fatigue limit states. To alleviate the simulation burden, machine learning methods can be useful. This article investigates the effect of machine learning methods on predicting the mooring line tension of a spar floating wind turbine. The OC3 Hywind wind turbine with a spar-buoy foundation and three mooring lines is selected and simulated with SIMA. A total of 32 sea states with irregular waves are considered. Artificial neural works with different constructions were applied to reproduce the time history of mooring tensions. The best performing network provides a strong average correlation of 71% and consists of two hidden layers with 35 neurons, using the Bayesian regularisation backpropagation algorithm. Sea states applied in the network training are predicted with greater accuracy than sea states used for validation of the network. The correlation coefficient is primarily higher for sea states with lower significant wave height and peak period. One sea state with a significant wave height of 5 meters and a peak period of 9 seconds has an average extreme value deviation for all mooring lines of 0.46%. Results from the study illustrate the potential of incorporating artificial neural networks in the mooring design process.

Correlation Enhanced Machine Learning Approach based Wave Height Prediction

2018 ◽  
Vol 4 (5) ◽  
pp. 10
Author(s):  
Ruchi Shrivastava ◽  
Dr. Krishna Teerth Chaturvedi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Wave Height ◽  
Significant Wave Height ◽  
Work Performance ◽  
Feature Reduction ◽  
Learning Approaches ◽  
Significant Wave ◽  
Wave Parameters ◽  
Height Prediction

The prediction of wave height is one of the major problems of coastal engineering and coastal structures. In recent years, advances in the prediction of significant wave height have been considerably developed using flexible calculation techniques. In addition to the traditional prediction of significant wave height, soft computing has explored a new way of predicting significant wave heights. This research was conducted in the direction of forecasting a significant wave height using machine learning approaches. In this paper, a problem of significant wave height prediction problem has been tackled by using wave parameters such as wave spectral density. This prediction of significant wave height helps in wave energy converters as well as in ship navigation system. This research will optimize wave parameters for a fast and efficient wave height prediction. For this Pearson’s, Kendall’s and Spearman’s Correlation Coefficients and Particle Swarm Optimization feature reduction techniques are used. So reduced features are taken into consideration for prediction of wave height using neural network. In this work, performance evaluation metrics such as MSE and RMSE values are decreased and gives better performance of classification that is compared with existing research’s implemented methodology. From the experimental results, it is observed that proposed algorithm gives the better prediction as compared to PSO feature reduction technique. So, it is also concluded that Co-relation enhanced neural network is better as compared to PSO based neural network with increased number of features.

Significant wave height and energy flux range forecast with machine learning classifiers

2015 ◽  
Vol 43 ◽  
pp. 44-53 ◽  
Author(s):  
J.C. Fernández ◽  
S. Salcedo-Sanz ◽  
P.A. Gutiérrez ◽  
E. Alexandre ◽  
C. Hervás-Martínez
Keyword(s):  
Machine Learning ◽  
Wave Height ◽  
Energy Flux ◽  
Significant Wave Height ◽  
Machine Learning Classifiers ◽  
Significant Wave ◽  
Learning Classifiers

Nowcasting significant wave height by hierarchical machine learning classification

2021 ◽  
Vol 242 ◽  
pp. 110130
Author(s):  
Demetris Demetriou ◽  
Constantine Michailides ◽  
George Papanastasiou ◽  
Toula Onoufriou
Keyword(s):  
Machine Learning ◽  
Wave Height ◽  
Significant Wave Height ◽  
Machine Learning Classification ◽  
Significant Wave

On the Sensitivity of Passive Multistatic Radar Amplitude and Doppler Measurements to Significant Wave Height

2021 ◽  
pp. 1-5
Author(s):  
Jeffrey D. Ouellette ◽  
William T. Bounds ◽  
David J. Dowgiallo ◽  
Jakov V. Toporkov ◽  
Paul A. Hwang
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Significant Wave ◽  
Multistatic Radar

scholarly journals Estimation of Significant Wave Heights from ASCAT Scatterometer Data via Deep Learning Network

2021 ◽  
Vol 13 (2) ◽  
pp. 195
Author(s):  
He Wang ◽  
Jingsong Yang ◽  
Jianhua Zhu ◽  
Lin Ren ◽  
Yahao Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wave Height ◽  
Significant Wave Height ◽  
Incidence Angle ◽  
Ocean Waves ◽  
Global Scale ◽  
Learning Technology ◽  
Sea State ◽  
Significant Wave ◽  
Wave Heights

Sea state estimation from wide-swath and frequent-revisit scatterometers, which are providing ocean winds in the routine, is an attractive challenge. In this study, state-of-the-art deep learning technology is successfully adopted to develop an algorithm for deriving significant wave height from Advanced Scatterometer (ASCAT) aboard MetOp-A. By collocating three years (2016–2018) of ASCAT measurements and WaveWatch III sea state hindcasts at a global scale, huge amount data points (>8 million) were employed to train the multi-hidden-layer deep learning model, which has been established to map the inputs of thirteen sea state related ASCAT observables into the wave heights. The ASCAT significant wave height estimates were validated against hindcast dataset independent on training, showing good consistency in terms of root mean square error of 0.5 m under moderate sea condition (1.0–5.0 m). Additionally, reasonable agreement is also found between ASCAT derived wave heights and buoy observations from National Data Buoy Center for the proposed algorithm. Results are further discussed with respect to sea state maturity, radar incidence angle along with the limitations of the model. Our work demonstrates the capability of scatterometers for monitoring sea state, thus would advance the use of scatterometers, which were originally designed for winds, in studies of ocean waves.

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