Preprints
https://doi.org/10.5194/egusphere-2022-1326
https://doi.org/10.5194/egusphere-2022-1326
05 Jan 2023
 | 05 Jan 2023

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

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

Abstract. Despite recent developments in geoscientific (e.g., physics/data-driven) models, effectively assembling multiple models for approaching a benchmark solution remains challenging in many sub-disciplines of geoscientific fields. Here, we proposed an automated machine learning-assisted ensemble framework (AutoML-Ens) that attempts to resolve this challenge. Details of the methodology and workflow of AutoML-Ens were provided, and a prototype model was realized with the key strategy of mapping between the probabilities derived from the machine learning classifier and the dynamic weights assigned to the candidate ensemble members. Based on the newly proposed framework, its applications for two real-world examples (i.e., mapping global soil water retention parameters and estimating remotely sensed cropland evapotranspiration) were investigated and discussed. Results showed that compared to conventional ensemble approaches, AutoML-Ens was superior across the datasets (the training, testing, and overall datasets) and environmental gradients with improved performance metrics (e.g., coefficient of determination, Kling-Gupta efficiency, and root mean squared error). The better performance suggested the great potential of AutoML-Ens for improving quantification and reducing uncertainty in estimates due to its two unique features, i.e., assigning dynamic weights for candidate models and taking full advantage of AutoML-assisted workflow. In addition to the representative results, we also discussed the interpretational aspects of the used framework and its possible extensions. More importantly, we emphasized the benefits of combining data-driven approaches with physics constraints for geoscientific model ensemble problems with high dimensionality in space and non-linear behaviors in nature.

Journal article(s) based on this preprint

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
Geosci. Model Dev., 16, 5685–5701, https://doi.org/10.5194/gmd-16-5685-2023,https://doi.org/10.5194/gmd-16-5685-2023, 2023
Short summary

Hao Chen et al.

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

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 

Journal article(s) based on this preprint

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
Geosci. Model Dev., 16, 5685–5701, https://doi.org/10.5194/gmd-16-5685-2023,https://doi.org/10.5194/gmd-16-5685-2023, 2023
Short summary

Hao Chen et al.

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Short summary
Effectively assembling multiple models for approaching a benchmark solution remains a long-standing issue for various geoscience domains. We here proposed an automated machine learning-assisted ensemble framework (AutoML-Ens) that attempts to resolve this challenge. Results demonstrated 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.