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10.1080/20964471.2018.1526057
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Machine learning for energy-water nexus: challenges and opportunities
Syed Mohammed Arshad Zaidi
Varun Chandola
Melissa R. Allen
Jibonananda Sanyal
Robert N. Stewart
Budhendra L. Bhaduri
Ryan A. McManamay
Taylor & Francis
Big Earth Data, 2018. doi:10.1080/20964471.2018.1526057
Machine learning
data
energy-water nexus
Journal
Big Earth Data
This manuscript has been co-authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).
2096-4471
2574-5417
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267
10.1080/20964471.2018.1526057
https://doi.org/10.1080/20964471.2018.1526057
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2018-12-03T16:37:58+05:30
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2020-10-23T20:29:04-07:00
Machine learning; data; energy-water nexus
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