Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 19
Articles | Volume 16, issue 19
https://doi.org/10.5194/gmd-16-5685-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-5685-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
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

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Latest update: 13 Dec 2024
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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 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.
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