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

Data sets

Global maps of soil water-retention parameters (field capacity and permanent wilting point) at different soil depths Hao Chen

Model code and software

AutoML-Ens Hao Chen

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.