Articles | Volume 16, issue 19
https://doi.org/10.5194/gmd-16-5685-2023
https://doi.org/10.5194/gmd-16-5685-2023
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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Cited articles

Abramowitz, G., Herger, N., Gutmann, E., Hammerling, D., Knutti, R., Leduc, M., Lorenz, R., Pincus, R., and Schmidt, G. A.: ESD Reviews: Model dependence in multi-model climate ensembles: weighting, sub-selection and out-of-sample testing, Earth Syst. Dynam., 10, 91–105, https://doi.org/10.5194/esd-10-91-2019, 2019. 
Araújo, M. B. and New, M.: Ensemble forecasting of species distributions, Trends Ecol. Evol., 22, 42–47, https://doi.org/10.1016/j.tree.2006.09.010, 2007. 
Bai, Y., Zhang, J., Zhang, S., Yao, F., and Magliulo, V.: A remote sensing-based two-leaf canopy conductance model: Global optimization and applications in modeling gross primary productivity and evapotranspiration of crops, Remote Sens. Environ., 215, 411–437, https://doi.org/10.1016/j.rse.2018.06.005, 2018. 
Bai, Y., Zhang, S., Bhattarai, N., Mallick, K., Liu, Q., Tang, L., Im, J., Guo, L., and Zhang, J.: On the use of machine learning based ensemble approaches to improve evapotranspiration estimates from croplands across a wide environmental gradient, Agr. Forest Meteorol., 298–299, 108308, https://doi.org/10.1016/j.agrformet.2020.108308, 2021. 
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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.