Articles | Volume 26, issue 16
https://doi.org/10.5194/hess-26-4469-2022
https://doi.org/10.5194/hess-26-4469-2022
Research article
 | 
30 Aug 2022
Research article |  | 30 Aug 2022

Forward and inverse modeling of water flow in unsaturated soils with discontinuous hydraulic conductivities using physics-informed neural networks with domain decomposition

Toshiyuki Bandai and Teamrat A. Ghezzehei

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-73', Silvio Gumiere, 24 Mar 2022
    • AC1: 'Reply on RC1', Toshiyuki Bandai, 30 Mar 2022
      • AC3: 'Reply on AC1', Toshiyuki Bandai, 07 Apr 2022
  • RC2: 'Comment on hess-2022-73', Anonymous Referee #2, 04 Apr 2022
    • AC2: 'Reply on RC2', Toshiyuki Bandai, 07 Apr 2022
      • AC4: 'Reply on AC2', Toshiyuki Bandai, 07 Apr 2022
  • RC3: 'Comment on hess-2022-73', Anonymous Referee #3, 22 Apr 2022
    • AC5: 'Reply on RC3', Toshiyuki Bandai, 21 May 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to revisions (further review by editor and referees) (23 May 2022) by Nunzio Romano
AR by Toshiyuki Bandai on behalf of the Authors (03 Jun 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (01 Jul 2022) by Nunzio Romano
RR by Anonymous Referee #3 (22 Jul 2022)
RR by Anonymous Referee #2 (05 Aug 2022)
ED: Publish as is (18 Aug 2022) by Nunzio Romano
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Short summary
Scientists use a physics-based equation to simulate water dynamics that influence hydrological and ecological phenomena. We present hybrid physics-informed neural networks (PINNs) to leverage the growing availability of soil moisture data and advances in machine learning. We showed that PINNs perform comparably to traditional methods and enable the estimation of rainfall rates from soil moisture. However, PINNs are challenging to train and significantly slower than traditional methods.