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

Ghaseminejad, Ali; Uddameri, Venkatesh

doi:https://doi.org/10.5194/hess-24-5759-2020

Articles | Volume 24, issue 12

https://doi.org/10.5194/hess-24-5759-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-24-5759-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 24, issue 12

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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Final revised paper (published on 03 Dec 2020)
Preprint (discussion started on 03 Apr 2020)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Review report, hess-2020-117', Anonymous Referee #1, 07 May 2020
- AC1: 'Response to Reviewer 1 Comments', Venkatesh Uddameri, 16 Jun 2020
RC2: 'Review: Physics-inspired integrated space-time Artificial Neural Networks for regional groundwater flow modeling', Anonymous Referee #2, 12 May 2020
- AC2: 'Response to Reviewer 2 Comments', Venkatesh Uddameri, 16 Jun 2020
EC1: 'Editor's final comment', Dimitri Solomatine, 16 Jun 2020

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

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.