Using artificial neural networks to predict future dryland responses to human and climate disturbances.

ABSTRACT: Land degradation and sediment remobilisation in dryland environments is considered to be a significant global environmental problem. Given the potential for currently stabilised dune systems to reactivate under climate change and increased anthropogenic pressures, identifying the role of external disturbances in driving geomorphic response is vitally important. We developed a novel approach, using artificial neural networks (ANNs) applied to time series of historical reactivation-deposition events from the Nebraska Sandhills, to determine the relationship between historic periods of sand deposition in semi-arid grasslands and external climatic conditions, land use pressures and wildfire occurrence. We show that both vegetation growth and sediment re-deposition episodes can be accurately es

SUBMITTER: Buckland CE 

PROVIDER: S-EPMC6405911 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature