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

A machine learning model for wave prediction based on support vector machine1

Qiang Liu1,2, Xingya Feng1,2, Tianning Tang3
Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, China.
2Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Shenzhen, China
3
Department of Engineering Science, University of Oxford, Oxford, UK

predict tides at three different locations on the east coast and west coastof India. Makarynskyy et al. (2005) used ANN to predict wave heights
and periods 3, 6, 12 and 24 hours in advance on the west coast of Portugal.
Kalra and Deo (2007) estimated the effective wave height, average wave
period and wind speed in coastal areas through ANN based on the values
collected from 19 offshore sites. In order to deal with the problem that
simple feedforward neural networks cannot consider the long-term
dependence on time series, Recurrent Neural Networks (RNNs) havebeen developed, which store the information of past values (Williamsand Zipser, 1989).

ABSTRACT: In this paper, we propose a least square support vectormachine (LSSVM) model to predict ocean wave elevations in a random
sea state. The frequency and time domain characteristics of historicalwave data are both considered in the proposed model. The wave data
following a JONSWAP spectrum measured through an indoor wave tankexperiment are used for the study. The measured time series weretransformed to frequency domain by the fast Fourier transform anddivided into five bands by filtering method. With the time seriescorresponding to each band, the LSSVM model is trained separately andused to predict future time series. The proposed model is shown togreatly extend the prediction time length, making it more effective to theapplication of the short-term real-time wave prediction.

However, a very long training of a model may cause the ANN method to
overfit and make the prediction result fall into the local optimum, which
is detrimental to the prediction result. Compared with the ANN method,
the support vector machine (SVM) method has a solid theoretical groundand does not come with problems such as probability measurement andthe law of large numbers. The SVM is essentially different from theexisting statistical methods. Mohandes et al. (2004) used the SVM
method for wind speed prediction. Asefa et al. (2006) used the SVM
method to make current prediction of complex ocean conditions.
Salcedo-Sanz et al. (2011) adopted the SVM method to carry out windspeed prediction for wind turbines of wind farms. Deka et al. (2014)
elaborated the application of SVM in the field of hydrology, and pointed
out that its ability to predict the atmospheric and marine environment had
been widely recognized by the community. Mahjoobi et al. (2009) used
the support vector regression (SVR) method to develop an effective
prediction model for ocean wave height. Ma Yu et al. (2018) utilized theleast square SVM (LSSVM) method to carry out model training and data
prediction for two sets of real ocean waves with typical wave periods of
8.7 seconds and 11.8 seconds. He was able to made accurate real-time
iterative prediction of wave elevation. To summarize, ML methods havebeen widely used in ocean wave prediction; but at present, the prediction
models of wave elevation is mostly based on the time series trend of data
points, without analysis of wave spectrum characteristics. Broad-band
spectral distribution of wave energy may increase the uncertainties intime series, raising the difficulty for prediction using machine learningmodels.

KEY WORDS: LSSVM; machine learning; filtering method; wave
prediction

INTRODUCTION
The real ocean waves are irregular, random and highly nonlinear.
Though theoretical and numerical models have been developed for longto understand the behavior of ocean waves, it remains a challenge tomake real-time predictions. A variety of prediction methods have beendeveloped. For computational models, wave parameters are simulated
and predicted by solving equations based on theoretical models, such as
the wave model (WAM) proposed by Group (1988). This method could
make good prediction for waves in deep-sea areas, but it has certain
limitations of application for nearshore areas with complex terrain. In
recent years, artificial intelligent (AI) methods such as machine learningtechniques have attracted much attention of researchers in various fields
because its emergence effectively makes up the shortcomings ofconventional prediction methods. Machine learning (ML) method could
make statistical prediction based on historical data, which has been
widely applied in the prediction of energy consumption, traffic flow,
rainfall, stock price and other fields, and has great potential in marine
engineering applications (Gopinath, 2015).
ML technique becomes an alternative to predict the time history of
surface wave elevations as in many other fields. Although the wavepropagation in the time domain is nonlinear, they are not completely‘random’ and can be predicted within a certain range of accuracy error.
Artificial neural network (ANN) is widely used in early studies for wave
prediction. Deo and Chaudhari (1998) utilized ANN technology toThe objective for this study is to develop a machine learning model for
ocean wave prediction that considers the characteristics of wave
elevations both in the time domain and frequency domain. We introduce
the idea of sub-frequency band prediction – the total time series are2026