2019/12/13 by Kelly Kochanski, Kochanski, Kelly, Divya Mohan +7
Agricultural and Biological Sciences · Earth and Planetary Sciences · Environmental Science · #Aeolian processes and effects #FOS: Computer and information sciences #Landslides and related hazards #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Soil erosion and sediment transport
paper · pdf · doi:10.48550/arxiv.1912.10798
openalex publication_date 2019/12/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 ∼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.