Articles | Volume 24, issue 12
Hydrol. Earth Syst. Sci., 24, 5759–5779, 2020
https://doi.org/10.5194/hess-24-5759-2020
Hydrol. Earth Syst. Sci., 24, 5759–5779, 2020
https://doi.org/10.5194/hess-24-5759-2020

Research article 03 Dec 2020

Research article | 03 Dec 2020

Physics-inspired integrated space–time artificial neural networks for regional groundwater flow modeling

Ali Ghaseminejad and Venkatesh Uddameri

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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to revisions (further review by editor and referees) (16 Jun 2020) by Dimitri Solomatine
AR by Anna Wenzel on behalf of the Authors (22 Jul 2020)  Author's response
ED: Referee Nomination & Report Request started (07 Aug 2020) by Dimitri Solomatine
RR by Anonymous Referee #2 (24 Aug 2020)
RR by Anonymous Referee #1 (05 Sep 2020)
ED: Publish as is (28 Sep 2020) by Dimitri Solomatine
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Short summary
While artificial neural networks (ANNs) have been used to forecast groundwater levels at single wells, they have not been constructed to forecast hydraulic heads in both space and time. This seminal study presents a modeling framework, guided by the governing physical laws, for building an integrated space–time ANN (IST–ANN) model for regional groundwater level predictions. IST–ANN shows promise for parsimoniously modeling regional-scale groundwater levels using available surrogate information.