Deep learning predictions of sand dune migration
This addresses the need for scalable forecasts of dune movement to protect roads, homes, and farmland in areas like the Navajo Nation and globally, though it is incremental as it builds on existing simulation data.
The researchers tackled the problem of predicting sand dune migration, which threatens infrastructure and land in arid regions, by testing deep learning algorithms (GAN and CNN) trained on simulated data, resulting in the GAN achieving predictions ten million times faster than existing models.
A dry decade in the Navajo Nation has killed vegetation, dessicated soils, and released once-stable sand into the wind. This sand now covers one-third of the Nation's land, threatening roads, gardens and hundreds of homes. Many arid regions have similar problems: global warming has increased dune movement across farmland in Namibia and Angola, and the southwestern US. Current dune models, unfortunately, do not scale well enough to provide useful forecasts for the $\sim$5\% of land surfaces covered by mobile sand. We test the ability of two deep learning algorithms, a GAN and a CNN, to model the motion of sand dunes. The models are trained on simulated data from community-standard cellular automaton model of sand dunes. Preliminary results show the GAN producing reasonable forward predictions of dune migration at ten million times the speed of the existing model.