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

Viewed

Total article views: 1,916 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,512 390 14 1,916 23 22
  • HTML: 1,512
  • PDF: 390
  • XML: 14
  • Total: 1,916
  • BibTeX: 23
  • EndNote: 22
Views and downloads (calculated since 03 Apr 2020)
Cumulative views and downloads (calculated since 03 Apr 2020)

Viewed (geographical distribution)

Total article views: 1,663 (including HTML, PDF, and XML) Thereof 1,659 with geography defined and 4 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 07 Dec 2021
Download
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.