Scientists have developed an advanced wave forecasting system that can run speedy simulations on a Raspberry Pi.
Using deep learning, analysts at IBM Research Ireland, Baylor University, and the University of Notre Dame built a system that far outpaces existing prediction models.
Costly and sluggish, traditional platforms require a supercomputer to calculate how tides, winds, and the ocean's varying depths influence the speed and height of waves.
The new deep learning-enhanced framework, however, generates forecasts up to 12,000 percent faster than conventional designs, according to IBM Research member Fearghal O'Donncha, who also tipped "a vastly increased" set of data input.
"Accurate forecasts of ocean wave heights and directions are a valuable resource for many marine-based industries," O'Donncha wrote in a blog post. "Many of these industries operate in harsh environments where power and computing facilities are limited.
"A solution to provide highly accurate wave condition forecasts at low computational cost is essential for improved decision making," he said.
Even artificial intelligence needs to learn, though.
The team put the time-honored Simulating WAves Nearshore (SWAN) model to work‚??generating training data (four years of forecasts, from April 2013 to July 2017) for their deep learning network.
A roaring success, the AI replicated images of more than 3,000 wave heights and periods with fewer errors than SWAN.
"Despite the huge reduction in computational expense, the new approach provides comparable levels of accuracy to the traditional physics-based SWAN model," O'Donncha boasted.
Not yet ready for primetime, the surrogate system is vetted only in Monterey Bay, Calif. To expand the model, researchers must repeat their training with new location-specific data‚??perhaps in collaboration with The Weather Company.
As O'Donncha told ZDNet, the forecasting firm could provide information "from a wide variety of locations," while IBM creates a suite of trained machines set to specific locations across the U.S. coastline.
"These models could then be readily provided based on a set of coordinates to enable exceedingly fast forecasts for any region within this location," he told the tech new blog.
The scientists hope their product will one day serve the likes of shipping companies, which can use "highly accurate forecasts" to determine the best route with the least fuel consumption and voyage time.