🌊 AI as a Monster Wave Detector!

Rogue waves, as tall as a ten-story building, often appear in the ocean. They can break apart or sink ships and damage oil platforms.

Legends of such "monster waves" have been around since ancient times. In 2015, it was possible to film this rare phenomenon: huge waves crashed on the Borgholm Dolphin platform in the North Sea. Still, until recently, scientists have not been able to fully explain the origin of this phenomenon or predict its occurrence.

Researchers from the University of Copenhagen have used artificial intelligence to discover these extreme waves’ causes and created an AI model that can predict how and when they can occur.

🔓 What are the findings? 

The researchers combined available data about ocean and sea movements, depth, and bathymetric information in their model. The buoys located at 158 different points along the US coast and overseas territories collected the wave data 24 hours a day.

Scientists ended up with a dataset containing 700 years’ worth of wave height and sea state information from more than a billion waves. They also registered more than 100,000 unusually high waves that could be defined as rogue ones.

The analysis showed that abnormal waves can appear at almost any time. They arise from the interaction between currents and winds when two wave systems cross and quickly reinforce one another. In this case, high crests and deep troughs emerge, making waves at least twice as high as the others. 

First identified in the 18th century, this phenomenon has now been validated by AI. With the creation of a new mathematical model, AI has achieved unprecedented accuracy in predicting rogue waves, far surpassing earlier forecasting techniques.

🚢 Why is it useful?

There are around 50,000 cargo ships sailing around the world at any given time. Using this publicly available algorithm, it is possible to receive warnings of dangerous waves and send this information to weather services.

As shipping companies plan their routes well in advance, they can use our algorithm to get a risk assessment of whether there is a chance of encountering dangerous rogue waves along the way. Based on this, they can choose alternative routes.

Dion Häfner, co-author of the study

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