Presentation by Jian Feng Choo (Technology Centre for Offshore and Marine, Singapore (TCOMS), Singapore), at the Delft3D User Days, during Delft Software Days - Edition 2022. Monday, 14 November 2022.

1. Prediction of Wind-Waves Using
Long-Short Term Memory (LSTM)
Models
Authors:
Jian Feng CHOO (Presenter)
Jeng Hei CHOW
Pavel TKALICH
Technology Centre for Offshore and Marine, Singapore (TCOMS)

2. 2
Contents
➢Introduction
➢Methodology
➢Results
➢Conclusion / Future Works

3. 3
Introduction
➢Introduction
• Objective
• Singapore’s Location
• Singapore’s Monsoon Season
• Long-Short Term Memory (LSTM)

4. 4
Objective
To develop a machine learning model that provides
cheap, fast and accurate prediction of waves for self-
driving vessels.
Singapore is the second largest port in the
world, and a large portion of its revenue is from
import and export.
These accidents are usually caused by human
error or unpredictable/constant changing
weather.

5. 5
Pacific
Ocean
Where is Singapore?
Indian
Ocean
South
China
Sea

6. 6
Singapore’s Monsoon Season
Period:
November to February
The winter air from China
blows across east Asia towards
the tropical ocean and
Australia (Summer).
Period:
June to August
Cold air from Australia
blows towards the
warmer tropical ocean
and the Asia
continent.

7. 7
Long Short Term Memory (LSTM) Neural Network
What is LSTM Neural Network?
• A deep neural network that is capable of learning
long term sequential data.
• LSTM are widely used in sentiment analysis,
language modelling, speech recognition and time
series prediction.
• Examples of applications:
o Netflix recommendations algorithms
o Financial market predictions (Stocks, Bonds,
Housing Pricings, etc)
o Chatbots

8. 8
Methodology
➢Methodology
• Source of Datasets
• Pre-processing
• Models schematics
• Model training parameters

9. 9
Source of Datasets
Parameters Significant Height
of Wind Wave
Wind 10m Above Sea
Level
Source ECMWF ECMWF
Type ERA5 Reanalysis ERA5 Reanalysis
Period 2010 - 2021 2010 - 2021
Temporal
Resolution
Hourly Hourly
Spatial
Resolution
0.5 Deg x 0.5 Deg 0.25 Deg x 0.25 Deg

10. 10
Data Pre-processing
Find strongest
correlation between
two points and its
respective lag.
Time Lag
Time Lag Cross-Correlation

11. 11
Data Pre-processing
U-velocity Wind
V-velocity Wind
Significant
Height of Wind
Wave
Wind Speed
Cross Correlation
between Wind Speed
and Significant Height of
Wind Wave
Cross Correlation
between Wind Direction
and Significant Height of
Wind Wave
Wind Direction
Split data
time series
into
respective
monsoon
seasons

12. 12
Northeast Monsoon (Nov – Mar)

13. 13
Inter Monsoon 1 (Apr – May)

14. 14
Southwest Monsoon (Jun – Aug)

15. 15
Inter Monsoon 2 (Sept – Oct)

16. 16
Input (Highest Correlation Point) / Output
Wind Wave
(Output)
UV Wind
(Input)

17. 17
LSTM Model Schematics
LSTM
t + hours prediction ahead
U wind
(Input)
V wind
(Input)
Wind Wave
(Output)
t - observations
t
t - observations
t

18. 18
LSTM Model Training Parameters
Training Test
10 Years 1 Year
2010 2021
2020

19. 19
Results
➢Results
• 10 Observation to Predict 1 Hour Ahead
• Multiple Observation, Multiple Prediction Ahead
• N – Highest Correlated Points

20. 20
10 Observation to Predict 1 Hour Ahead (Time series)
Predicted VS Actual
Significant
Height
Wind
Wave
(m)
Time

21. 21
10 Observation to Predict 1 Hour Ahead (Scatter Plot)
Predicted
Actual
Actual vs Predicted Values
R^2 = 0.958
RMSE = 0.0732

22. 22
Multiple Observation, Multiple Prediction Ahead
Observe
(Hours)
Prediction ahead
(Hours)
RMSE R^2
10 1 0.0732 0.958
10 2 0.0987 0.9235
10 5 0.1491 0.8254
20 1 0.0733 0.9581
20 2 0.1064 0.9111
20 5 0.1437 0.8147
30 1 0.0734 0.9578
30 2 0.1191 0.9007
30 5 0.1472 0.8299
• Increasing
observations doesn’t
increase accuracy.
• Increasing prediction
ahead decreases
accuracy.

23. 23
N – Highest Correlated Points
Wind Wave
(Output) UV Wind
(Input)

24. 24
N – Highest Correlated Points
Number of Highest Correlated
Points (Input)
RMSE R^2
1 0.07585 0.9548
3 0.07067 0.9613
5 0.06939 0.9618
• Increasing the number of input of highly correlated points
increases the accuracy, but not significant.

25. 25
Conclusion and Future Works
Conclusion
1. LSTM shows promising results to predict wind waves from wind.
2. Increase in observations have no effect in prediction accuracy.
3. Accuracy decreases for increasing ahead prediction.
4. Increasing the number input points with “n” highest correlation
points increases the accuracy, however the computational speed
to train the model increases.
Future Works
1. Embed physics about wind wave interaction into LSTM model.
2. Using ConvLSTM model for 2D time series prediction.