Articles | Volume 26, issue 12
Research article
28 Jun 2022
Research article |  | 28 Jun 2022

Integrating process-related information into an artificial neural network for root-zone soil moisture prediction

Roiya Souissi, Mehrez Zribi, Chiara Corbari, Marco Mancini, Sekhar Muddu, Sat Kumar Tomer, Deepti B. Upadhyaya, and Ahmad Al Bitar


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-63', Anonymous Referee #1, 21 Mar 2022
    • AC1: 'Reply on RC1', Roiya Souissi, 02 May 2022
  • RC2: 'Comment on hess-2022-63', Anonymous Referee #2, 07 Apr 2022
    • AC2: 'Reply on RC2', Roiya Souissi, 02 May 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (further review by editor and referees) (16 May 2022) by Philippe Ackerer
AR by Roiya Souissi on behalf of the Authors (30 May 2022)  Author's response 
EF by Polina Shvedko (02 Jun 2022)  Manuscript   Author's tracked changes 
ED: Publish subject to technical corrections (10 Jun 2022) by Philippe Ackerer
AR by Roiya Souissi on behalf of the Authors (10 Jun 2022)  Manuscript 
Short summary
In this study, we investigate the combination of surface soil moisture information with process-related features, namely, evaporation efficiency, soil water index and normalized difference vegetation index, using artificial neural networks to predict root-zone soil moisture. The joint use of process-related features yielded more accurate predictions in the case of arid and semiarid conditions. However, they have no to little added value in temperate to tropical conditions.