Articles | Volume 18, issue 19
https://doi.org/10.5194/gmd-18-6903-2025
https://doi.org/10.5194/gmd-18-6903-2025
Development and technical paper
 | 
08 Oct 2025
Development and technical paper |  | 08 Oct 2025

Enhanced land subsidence interpolation through a hybrid deep convolutional neural network and InSAR time series

Zahra Azarm, Hamid Mehrabi, and Saeed Nadi

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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 gmd-2024-15', Anonymous Referee #1, 21 Jun 2024
  • CEC1: 'Comment on gmd-2024-15', Astrid Kerkweg, 02 Jul 2024
  • CC1: 'Comment on gmd-2024-15', Yusof Ghiasi, 22 Aug 2024
  • RC2: 'Comment on gmd-2024-15', Anonymous Referee #2, 11 Feb 2025

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Hamid Mehrabi on behalf of the Authors (19 Feb 2025)  Author's response 
EF by Katja Gänger (21 Feb 2025)
EF by Katja Gänger (21 Feb 2025)
EF by Katja Gänger (24 Feb 2025)  Author's tracked changes 
EF by Katja Gänger (24 Feb 2025)  Manuscript 
ED: Referee Nomination & Report Request started (25 Feb 2025) by Rohitash Chandra
ED: Publish subject to technical corrections (14 Jul 2025) by Rohitash Chandra
AR by Hamid Mehrabi on behalf of the Authors (17 Jul 2025)  Manuscript 
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Short summary
The article introduces a new method to estimate land subsidence using deep convolutional neural networks (CNNs) and persistent scatterer interferometric synthetic aperture radar (PSInSAR), addressing the limitations of traditional methods. It focuses on Isfahan Province, Iran, and demonstrates substantial improvement over conventional techniques. The deep CNN method showed a 70 % enhancement in subsidence prediction, with the study area experiencing over 38 cm of subsidence between 2014 and 2020.
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