Articles | Volume 16, issue 19
Development and technical paper
12 Oct 2023
Development and technical paper |  | 12 Oct 2023

Dynamically weighted ensemble of geoscientific models via automated machine-learning-based classification

Hao Chen, Tiejun Wang, Yonggen Zhang, Yun Bai, and Xi Chen


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-1326', Anonymous Referee #1, 06 Feb 2023
    • CC1: 'Reply on RC1', Hao Chen, 03 May 2023
    • AC1: 'Reply on RC1', Tiejun Wang, 26 Jun 2023
  • RC2: 'Comment on egusphere-2022-1326', Anonymous Referee #2, 25 May 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Tiejun Wang on behalf of the Authors (26 Jun 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (11 Jul 2023) by Klaus Klingmüller
RR by Anonymous Referee #2 (25 Jul 2023)
RR by Anonymous Referee #1 (04 Aug 2023)
ED: Publish subject to minor revisions (review by editor) (18 Aug 2023) by Klaus Klingmüller
AR by Tiejun Wang on behalf of the Authors (20 Aug 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (07 Sep 2023) by Klaus Klingmüller
AR by Tiejun Wang on behalf of the Authors (08 Sep 2023)  Manuscript 
Short summary
Effectively assembling multiple models for approaching a benchmark solution remains a long-standing issue for various geoscience domains. We here propose an automated machine learning-assisted ensemble framework (AutoML-Ens) that attempts to resolve this challenge. Results demonstrate the great potential of AutoML-Ens for improving estimations due to its two unique features, i.e., assigning dynamic weights for candidate models and taking full advantage of AutoML-assisted workflow.