Deterministic wave prediction for unidirectional sea-states in real-time using Artificial Neural Network

Law, Yun Zhi; Santo, H.; Lim, Kian Yew; Chan, Eng Soon

doi:10.1016/j.oceaneng.2019.106722

Cited by 55 publications

(24 citation statements)

References 17 publications

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“…In common with much other work on deterministic forecasting (see e.g. Desmars et al 2018;Klein et al 2020;Law et al 2020), engineering design spectra will be used to generate the synthetic sea surfaces herein. These design spectra provide the basic parameters of the synthetic sea surface.…”

Section: Synthetic Sea Surfaces and Measurementsmentioning

confidence: 99%

“…Law et al. (2020) recently used an artificial neural network trained with HOS simulations to produce predictions. Desmars et al.…”

Section: Introductionmentioning

confidence: 99%

“…Many existing applications (e.g. Connell et al 2015;Hilmer & Thornhill 2016;Al-Ani, Belmont & Christmas 2020) of deterministic forecasting, both research and commercial, make use of X-band radar backscatter (see Young, Rosenthal & Ziemer 1985;Lee et al 1995;and others), although approaches based on LIDAR have also been advanced (see e.g. Belmont et al 2007;Grilli, Guérin & Goldstein 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Klein et al (2019) subsequently investigated the use of HOS for a variety of different sea states, and used a surface similarity parameter to quantify the accuracy of the forecast. Law et al (2020) recently used an artificial neural network trained with HOS simulations to produce predictions. Desmars et al (2020) have developed a nonlinear Lagrangian model (see Guérin et al 2019) and employed this for forecasting from non-uniformly spaced data from both HOS simulations and experiments.…”

Section: Introductionmentioning

confidence: 99%

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Deterministic wave forecasting with the Zakharov equation

Stuhlmeier

Stiassnie

2021

J. Fluid Mech.

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Section: Synthetic Sea Surfaces and Measurementsmentioning

confidence: 99%

“…Law et al. (2020) recently used an artificial neural network trained with HOS simulations to produce predictions. Desmars et al.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deterministic wave forecasting with the Zakharov equation

Stuhlmeier

Stiassnie

2021

J. Fluid Mech.

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show abstract

“…In addition to the traditional physical model [24,25], novel methods and models based on big data and machine learning have also been presented. Law [26] carried out wave prediction over a large distance downstream using artificial neural network, introducing machine learning algorithm into ocean engineering. Desouky [27] utilized non-linear autoregressive with exogenous input network to predict the surface elevation with the help of an ahead located sensor.…”

Section: Introductionmentioning

confidence: 99%

Capture Power Prediction of the Frustum of a Cone Shaped Floating Body Based on BP Neural Network

Wang

Liu

Bai

et al. 2021

JMSE

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How to improve the power generation of wave energy converters (WEC) has become one of the main research objectives in wave energy field. This paper illustrates a framework on the use of back propagation (BP) neural network in predicting capture power of the frustum of a cone shaped floating body. Mathematical model of single floating body is derived, and radius, semi-vertical angle, mass, submergence depth, power take-off (PTO) damping coefficient, and stiffness coefficient are identified as key variables. Commercial software ANSYS-AQWA is used for numerical simulations to obtain hydrodynamic parameters, and then capture power is calculated by these parameters. A database containing 100 samples is established by Latin hypercube sampling (LHS) method, and a simple feature study is conducted. A BP neural network model with high accuracy is designed and trained for predictions based on built database. The results show that forecasting results and desired outputs are in great agreement with error percentage not greater than 4%, correlation coefficient (CC) greater than 0.9, P value close to 1, and root mean square error (RMSE) less than 139 W. The proposed method provides a guideline for designers to identify basic parameters of the floating body and system damping coefficient.

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Maximum wave height hindcasting using ensemble linear-nonlinear models

Tür

2020

Theor Appl Climatol

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Deterministic wave prediction for unidirectional sea-states in real-time using Artificial Neural Network

Cited by 55 publications

References 17 publications

Deterministic wave forecasting with the Zakharov equation

Deterministic wave forecasting with the Zakharov equation

Capture Power Prediction of the Frustum of a Cone Shaped Floating Body Based on BP Neural Network

Maximum wave height hindcasting using ensemble linear-nonlinear models

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