How ocean buoys and neural networks can help predict rogue waves
The ocean's unpredictability is magical and even poetic. But when it puts people in danger, information can be lifesaving.
The fear behind these unexpected and massive walls of water has stormed the human spirit for centuries.
Katsushika Hokusai depicted them in "The Great Wave off Kanagawa" around 1830-1983.
Now, a study by Thomas Breunung and Balakumar Balachandran from the University of Maryland, published in Scientific Reports, unveiled a tool for predicting rogue waves using buoy measurements in conjunction with neural networks.
Rogue waves, also known as freak waves, are sporadic, unusually large, and unexpected waves that present significant dangers to ships, offshore platforms, and other maritime structures.
Despite being widely recognized, knowing when these waves will pop suddenly has been extremely difficult, posing a severe risk to maritime operations.
Deconstructing Unpredictability
The formation of rogue waves is often attributed to the superposition of multiple smaller waves, leading to a single, large wave through constructive interference.
The phenomenon relies on the synchronization of wave phases, which are typically unknown and assumed to be random in current ocean models.
Because these models do not provide information on wave phases, predicting rogue waves has been considered impossible until recent years.
Another potential mechanism for rogue wave formation is the Benjamin-Feir instability, where a wave train becomes unstable due to specific modulations.
While theoretically, it could allow for prediction, real-world evidence supporting this mechanism is limited, leading to skepticism about its practical relevance.
Various methods have been proposed to forecast freak waves based on average sea conditions, such as significant wave height and skewness.
However, these indicators have shown a poor correlation with abnormally large wave occurrences in practice.
More recently, a data-driven approach suggested the potential for real-time predictions of individual waves, but a systematic assessment of its accuracy and predictive horizon was lacking.
Identifying Rogue Waves with Buoys and LTSM Networks
The study by Breunung and Balachandran aims to overcome these challenges by leveraging field data from ocean buoys to predict rogue waves.
Researchers used a large dataset of sea surface measurements and applied neural networks, specifically long short-term memory (LSTM) networks, to identify patterns preceding freak wave events.
The team worked with data from 172 buoys maintained by the Coastal Data Information Program (CDIP).
These buoys record vertical displacements of the sea surface, which can be analyzed to identify rogue waves.
The dataset includes billions of wave measurements, providing a rich source of information for training the neural networks.
Freak waves were identified by calculating the significant wave height (four times the standard deviation of sea surface elevation) and comparing individual wave heights to this benchmark.
The data was divided into segments that either preceded a freak wave or did not, creating a balanced dataset for training the neural networks.
LSTM networks, a type of recurrent neural network designed to handle sequential data, were chosen for their ability to learn temporal patterns.
A neural network is a type of computer program designed to mimic how the human brain processes information.
It features layers of interconnected nodes, or "neurons," each performing simple calculations.
These neurons are structured into three main types of layers: an input layer, one or more hidden layers, and an output layer.
In this particular case, neural networks were trained to distinguish between wave patterns that precede rogue waves and those that do not.
75 Percent Accuracy
Thomas Breunung and Balakumar Balachandran concluded that trained neural networks could predict rogue waves with significant accuracy.
For a warning time of 1 minute, the networks correctly predicted three out of four (75 percent) freak waves.
Extending the warning time to 5 minutes slightly reduced accuracy to 73 percent correct predictions.
The networks also demonstrated good generalization ability, successfully predicting rogue waves in new buoy locations not included in the training data.
Scientists believe this indicates the potential for widespread application of the predictive model.
Future Updates
The study shows that rogue waves can be predicted with a reasonable degree of accuracy using neural networks and field data from buoys.
This represents a significant step towards practical uber-large wave forecasting systems, which could greatly enhance maritime safety.
Future research could improve prediction accuracy and advance warning times by using more sophisticated neural network architectures, incorporating additional physical data, or increasing the spatial resolution of measurements.
More data will be essential to refine these models and expand their predictive capabilities.