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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 e...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1038/s41598-019-40429-5

Authors


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Institution:
University of Oxford
Division:
SSD
Department:
SOGE
Oxford college:
Jesus College
Role:
Author
More by this author
Institution:
University of Oxford
Division:
Social Sciences Division
Department:
SOGE
Sub department:
Geography
Role:
Author
More by this author
Institution:
University of Oxford
Division:
Social Sciences Division
Department:
SOGE
Sub department:
Geography
Role:
Author
Publisher:
Nature Research Publisher's website
Journal:
Scientific Reports Journal website
Volume:
9
Article number:
3855
Publication date:
2019-03-07
Acceptance date:
2019-02-11
DOI:
ISSN:
2045-2322
Source identifiers:
969966
Pubs id:
pubs:969966
UUID:
uuid:2987622a-b462-4eb3-8c72-90ea09306b4a
Local pid:
pubs:969966
Deposit date:
2019-02-11

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