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Combining multi-modal statistics for welfare prediction using deep learning

Abstract:

In the context of developing countries effective groundwater resource management is often hindered by a lack of data integration between resource availability, water demand, and the welfare of water users. As a consequence, drinking water related policies and investments, while broadly beneficial, are unlikely to be able to target the most in need. In order to find the households in need we need to estimate their welfare status first. However, the current practices for estimating welfare need...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.3390/su11226312

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
SSD
Department:
SOGE
Sub department:
Smith School
Role:
Author
More by this author
Institution:
University of Oxford
Division:
SSD
Department:
SOGE
Sub department:
Environmental Change Institute
Role:
Author
More by this author
Institution:
University of Oxford
Division:
SSD
Department:
SOGE
Sub department:
Smith School
Role:
Author
Publisher:
MDPI
Journal:
Sustainability More from this journal
Volume:
11
Issue:
22
Article number:
6312
Publication date:
2019-11-11
Acceptance date:
2019-11-02
DOI:
EISSN:
2071-1050
Language:
English
Keywords:
Pubs id:
pubs:1069793
UUID:
uuid:aa10e810-c31a-42ce-97d5-911aead2c3da
Local pid:
pubs:1069793
Source identifiers:
1069793
Deposit date:
2019-11-04

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