Proceedings of the Thirty-second (2022) International Ocean and Polar Engineering Conference
Shanghai, China, June 5–10, 2022
Copyright © 2022 by the International Society of Offshore and Polar Engineers (ISOPE)
ISBN 978-1-880653-81-4; ISSN 1098-6189

www.isope.org

Real-Time Prediction of Near-Field Wave from Far-Field Measurements with Neural Network for UniDirectional and Multi-Directional Random Waves
Tuyen Quang Le, Ha My Dao, Xin Lu and Inez Bangar
Institute of High Performance Computing
Singaporefew minutes but being crucial in operations of ocean platform. Theseinclude takeoff and landing of helicopters, dynamic positioning offloating structures or vessels and rescues operations. To meet thesepractical applications, both accuracy and speed in wave prediction arerequired. Researchers therefore have strongly focused on using ML tobuild a prediction model for ocean waves. The wave height can bepredicted accurately in several ML models: Long short-term memory(LSTM), the Artificial neural network (ANN) and hybrid models withcombination of nonlinear autoregressive exogenous (NARX) (Ren etal., 2018; Fan et al., 2020). The phase-resolved prediction in multidirectional steepness wave has hitherto been challenged for researchers
even though there are some reports for uni-directional long-cress wave
(Law et al., 2020; Duan et al., 2020).

ABSTRACTIn this study, we explore the ability of the full connected neuralnetwork (FCNN) in prediction of phase-resolved uni-directional andmulti-directional waves in various wave conditions in a typical offshore
engineering application. The neural network models are trained on
random waves that are generated by the JONSWAP spectrum withsignificant wave height and peak spectral wave period taken from theNorth Sea wave data. The selected wave steepness varies from 0.01,
0.017 to 0.038, which covers mild to steep waves. Linear Wave Theory(LWT) could provide reasonable accuracy for mild wave conditions but
less accurate for steeper waves. The FCNN gives more accurate resultsin phase-resolved prediction of average steepness cases in multi
directional waves.

In this study, we use a fully connected neural network (FCNN) topredict 30-40s periods of phase-resolved waves at a location far frommeasurement point. The FCNN has shown to give more accurate results
in predictions of phase-resolved waves than LWT in Law et al., (2020).
Here, the performance of the FCNN with different arrangements input
data is evaluated in various settings of uni-directional and multidirectional waves created by the Jonswap spectrum. The statisticalquantities, Hs and Tp of sea-state in the North Sea are selected in ourstudy.

KEY WORDS: Multi-directional wave prediction, predictable zone,
fully connected neural network (FCNN), Jonswap spectrum.

INTRODUCTION
Wave prediction in ocean and offshore engineering has been advancing
rapidly. Several models (WAMDI, 1988; Booij et al., 1999) can giveinformation of statistical quantities: significant wave height (Hs), peak
spectral period (Ts) and mean wave direction. However, rapid phaseresolved prediction of wave propagation has still been a challenging
topic for researchers due to fully non-linear physics. In the initial effortof phase-resolved prediction, the linear wave theory (LWT) based on
potential flow was proposed in early 1990, it is simple but only works
for small steepness and short-term prediction (Morris et al., 1998;
Ruban, 2016).

METHODOLOGY
Theoretical Predictable Zone (TPZ)
The LWT predicts wave information based on combining the wavehydrodynamics and signal processing. First, LWT transforms irregularwave data into “more cosine” form and acquires frequency
characteristics by using Fast Fourier Transforms (FFT). The amplitude
and phase information are calculated by DFT as in Eq 1. The predictedwave elevation at any locations can be done by using Eq 2. The TPZ isdetermined from the maximum and minimum group velocity of arandom wave group following Qi et al. (2018). The propagation
velocities of the group cosine waves are first calculated based on lineardispersion relation in space and time according to the reference water
depth. Based on a correlation between the dispersion and groupvelocities, arrival times at the predicted position of the fastest andlowest frequencies are determined. Finally, the TPZ at the predicted
position is defined as wave information in the range between lowest

Thanks to advances in the machine learning (ML) in computer science,
the non-linear problem in engineering can be solved or partly solved.
The neural network model can quickly predict a result in online stage
after being trained on available data in offline stage. More recently,
with advanced neural network being rapidly developed, its application
has widely used computer vision, fluid dynamics and control.
Therefore, deterministic wave prediction using ML in real-time has
become possible. The short-term deterministic wave prediction plays animportant role in the efficiency of maneuver operations which take only

2031