Deep learning predictions of sand dune migration

TL;DR

This paper explores the use of deep learning models, specifically GANs and CNNs, trained on simulated data to predict sand dune migration more efficiently than traditional models, addressing environmental challenges in arid regions.

Contribution

It introduces a novel application of deep learning algorithms to model sand dune movement, significantly improving prediction speed over existing cellular automaton models.

Findings

GAN achieved reasonable forward predictions of dune migration

Predictions are produced at ten million times the speed of traditional models

Models trained on simulated data can effectively forecast dune movement

Abstract

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.