Large resources are required to provide vessels in the polar seas with warnings about the spread of sea ice. Artificial intelligence on a regular laptop may make these warnings cheaper, faster, and available for everyone, say researchers at The Arctic University of Norway.
For vessels that journey into the polar seas, keeping control of the spread of sea ice is critical, which means that large resources are spent to collect data and determine future developments to provide reliable sea ice warnings, explains a statement.
Sindre Markus Fritzner of the The Arctic University of Norway recently submitted a doctoral thesis where he looked at the option of using artificial intelligence on a regular laptop to make ice warnings faster, better, and more accessible than they are today.
The ice warnings used today are traditionally based on dynamic computer models that are fed with satellite observations of the ice cover, and whatever updated data can be gathered about ice thickness and snow depth. This generates considerable amounts of data, which then need to be processed by powerful supercomputers to provide calculations.
This is a limited and costly resource, which makes these warnings impossible to do without access to the right resources.
In Fritzner’s work, he loaded in machine data to see how one specific week will unfold, and then data for how it will look one week later on, explains the statement. “Thus, it is the coherence in the development between these weeks that the machine learns itself, and in this way it can predict how it evolves,” Fritzner says in the statement. When fully developed, such an algorithm will demand far less computing power than the traditional physical model.
“If you use artificial intelligence and have a fully trained model, you can run such a calculation on a regular laptop”, Fritzner says. This opens up for several fields of usage, one of them being more precise weather reports in The High North. Fritzner also points out that this can be used by the shipping industry that operate close to the marginal ice zone, and that this is a form of traffic that will only increase.
“One example is cruise traffic, where it will be very important for the cruise vessels to know where the ice is, and where it will move in the next couple of days”, Fritzner says.
Although the research so far looks promising, the results are still not as good as the traditional methods, but the evolution of machine learning/artificial intelligence is reaching full steam, and Fritzner has no doubts about its potential.
Image credit: Ronald Woan, flickr/Creative Commons