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

Forecasting coastal significant wave heights using Long Short-Term Memory Network Model
Qinze Chen, Jiaming Qu*, Yuchi Hao
CCCC National Engineering Research Center of Dredging Technology and Equipment.
Shanghai, China*Corresponding author

KEY WORDS: ocean engineering; long short-term memory
network model; wave height forecastingWith the development of machine learning techniques, it has been widelyused for wave height prediction. Feed-forward network had been proventhat it has a more general and adaptable capability in real-time wave
height prediction (Deo, Sridhar and Naidu, 1998). Then, studies haveshown that recurrent neural network (RNN) performed better insignificant wave height prediction (Mandal and Prabaharan, 2006;
Sadeghifar et al., 2017). RNN excels at extracting features from the input
data of long sequences but still suffers from vanishing and exploding
gradient problem. Besides, RNN updates the network with the latest
information, it still performs poorly for sequence predictions that aredependent on long-term dependence. To solve this problem, an idea ofincreasing networked storage arose. LSTM (long short-term-memory)
networks with special implicit units were first proposed, and their natural
behavior is to save input in a long term (Hochreiter and Schmidhuber,1997). LSTM is widely used in sequence studies with strong temporalcorrelation, such as speech recognition, handwriting recognition, andimage information recognition (Graves and Jaitly, 2014; Graves et al.,2009; Aggarwal, 2018). Also, it has performed well in meteorologicalfields such as sea surface temperature prediction (Xiao et al., 2019) andweather forecasting (Salman et al., 2018).
In this paper, a variety of sequence-to-sequence neural networks with
LSTM were applied to reconstruct and predict coastal significant wave
heights, and models with different input and output structures werediscussed. In addition, a model comparison of several machine learning
methods was applied on the coastal significant wave heights data tobenchmark the LSTM network. The main contributions of this study are
as follows:

INTRODUCTION⚫

ABSTRACTThe real time forecasting of wave features is very important to theocean operations. The traditional way for forecasting wave heightis based on physical process, it needs a lot of fundamental data toestablish the model, which is the biggest obstacle to the model’s
accuracy. To solve this problem, we used a long short-term
memory network model to forecast coastal wave features indifferent time series. Furthermore, compared it to linear modeland support vector regression model with various kernel
functions. Root Mean Square Error (RMSE) and correlationcoefficient (R) were used for evaluating the model. The results
showed that compared to other 4 models long short-term memory
network model shows a higher accuracy in forecasting significant
wave height; the correlation coefficient is 0.93. With theextension though of the forecasting leading time, the accuracy
decreased. The correlation coefficient of forecasting next 3 hours’
wave height equals to that of forecasting 1 hour’s wave height,
and it is 0.98. In summary, LSTM model has engineering value
in small-scale wave forecasting for trailing suction dredger
constructing.

Waterway transportation, dredging, reclamation and other projects arehighly sensitive operations to the marine environment. During theprocess of operations, real-time marine environment should be mastered
to ensure that the projects are carried out safely and efficiently.
The existing research of forecasting coastal wave height is based mostly
on numerical models, which require comprehensive climatic andgeographical data to build and consume significant computational power
and time (Thomas and Dwarakish, 2015). However, it is not applicablefor most of the construction projects. Therefore, it is necessary to build
a wave prediction model that is computationally efficient and can beapplied across different wave condition.

⚫

LSTM network
We proposed a LSTM model to feasibly forecast the coastal
significant wave height, which had a good performance on certain
time steps forecasts.
Multi models comparison
We further investigated a comparison of different models. It turned
out that the LSTM had distinct superiority over the other machinelearning models.

2 Study site and data set
With the opening of the new Eurasian land bridge and the promotion of2641