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
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.”
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 said.
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 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.