Deep learning predictions of sand dune migration (Proposals Track)

Kelly Kochanski (University of Colorado Boulder); Divya Mohan (University of California Berkeley); Jenna Horrall (James Madison University); Ghaleb Abdulla (Lawrence Livermore National Laboratory)

Abstract

Climate change is making many desert regions warmer, drier, and sandier. These conditions kill vegetation, and release once-stable sand into the wind, allowing it to form dunes that threaten roads, farmland, and solar panel installations. With enough warning, people can mitigate dune damages by moving infrastructure or restoring vegetation. Current dune simulations, however, do not scale well enough to provide useful forecasts for the ~5% of Earth's land surface that is covered by mobile sands. We propose to train a deep learning simulation to emulate the output of a community-standard physics-based dune simulation. We will base the new model on a GAN-based video prediction model with an excellent track record for predicting spatio-temporal patterns to model, and use it to simulate dune topographies over time. Our preliminary work indicates that the new model will run up to ten million times faster than existing dune simulations, which would turn dune modelling from an exercise that covers a handful of dunes to a practical forecast for large desert regions.