Articles | Volume 15, issue 7
Geosci. Model Dev., 15, 2839–2858, 2022
https://doi.org/10.5194/gmd-15-2839-2022
Geosci. Model Dev., 15, 2839–2858, 2022
https://doi.org/10.5194/gmd-15-2839-2022
Development and technical paper
07 Apr 2022
Development and technical paper | 07 Apr 2022

KGML-ag: a modeling framework of knowledge-guided machine learning to simulate agroecosystems: a case study of estimating N2O emission using data from mesocosm experiments

Licheng Liu et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on gmd-2021-317', Ather Abbas, 05 Nov 2021
    • AC1: 'Reply on CC1', Zhenong Jin, 05 Nov 2021
      • CC2: 'Reply on AC1', Ather Abbas, 23 Nov 2021
        • AC2: 'Reply on CC2', Zhenong Jin, 18 Feb 2022
  • RC1: 'Comment on gmd-2021-317', Anonymous Referee #1, 19 Nov 2021
  • RC2: 'Comment on gmd-2021-317', Anonymous Referee #2, 23 Nov 2021
  • RC3: 'Comment on gmd-2021-317', Anonymous Referee #3, 25 Nov 2021
  • AC3: 'Author response to review comments', Zhenong Jin, 18 Feb 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Anna Mirena Feist-Polner on behalf of the Authors (01 Mar 2022)  Author's response    Manuscript
ED: Publish subject to minor revisions (review by editor) (02 Mar 2022) by Sam Rabin
AR by Zhenong Jin on behalf of the Authors (09 Mar 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish subject to minor revisions (review by editor) (10 Mar 2022) by Sam Rabin
AR by Zhenong Jin on behalf of the Authors (10 Mar 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (11 Mar 2022) by Sam Rabin
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Short summary
By incorporating the domain knowledge into a machine learning model, KGML-ag overcomes the well-known limitations of process-based models due to insufficient representations and constraints, and unlocks the “black box” of machine learning models. Therefore, KGML-ag can outperform existing approaches on capturing the hot moment and complex dynamics of N2O flux. This study will be a critical reference for the new generation of modeling paradigm for biogeochemistry and other geoscience processes.