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.
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.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
12 Oct 2023
Development and technical paper |
| 12 Oct 2023
| 12 Oct 2023
Dynamically weighted ensemble of geoscientific models via automated machine-learning-based classification
Institute of Surface-Earth System Science, School of Earth System
Science, Tianjin University, Tianjin, 300072, China
Tianjin Key Laboratory of Earth Critical Zone Science and Sustainable Development in Bohai Rim, Tianjin University, Tianjin, 300072, China
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China
Tiejun Wang
CORRESPONDING AUTHOR
Institute of Surface-Earth System Science, School of Earth System
Science, Tianjin University, Tianjin, 300072, China
Tianjin Key Laboratory of Earth Critical Zone Science and Sustainable Development in Bohai Rim, Tianjin University, Tianjin, 300072, China
Tianjin Bohai Rim Coastal Earth Critical Zone National Observation and Research Station, Tianjin University, Tianjin, 300072, China
Yonggen Zhang
Institute of Surface-Earth System Science, School of Earth System
Science, Tianjin University, Tianjin, 300072, China
Tianjin Key Laboratory of Earth Critical Zone Science and Sustainable Development in Bohai Rim, Tianjin University, Tianjin, 300072, China
Hebei Technology Innovation Center for Remote Sensing Identification of Environmental Change, School of Geographic Sciences, Hebei Normal University, Shijiazhuang, 050024, China
Xi Chen
Institute of Surface-Earth System Science, School of Earth System
Science, Tianjin University, Tianjin, 300072, China
Tianjin Key Laboratory of Earth Critical Zone Science and Sustainable Development in Bohai Rim, Tianjin University, Tianjin, 300072, China
Tianjin Bohai Rim Coastal Earth Critical Zone National Observation and Research Station, Tianjin University, Tianjin, 300072, China
Viewed
Total article views: 6,332 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 05 Jan 2023)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 4,815 | 1,351 | 166 | 6,332 | 416 | 188 | 320 |
- HTML: 4,815
- PDF: 1,351
- XML: 166
- Total: 6,332
- Supplement: 416
- BibTeX: 188
- EndNote: 320
Total article views: 4,034 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 12 Oct 2023)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 3,453 | 487 | 94 | 4,034 | 162 | 114 | 170 |
- HTML: 3,453
- PDF: 487
- XML: 94
- Total: 4,034
- Supplement: 162
- BibTeX: 114
- EndNote: 170
Total article views: 2,298 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 05 Jan 2023)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 1,362 | 864 | 72 | 2,298 | 254 | 74 | 150 |
- HTML: 1,362
- PDF: 864
- XML: 72
- Total: 2,298
- Supplement: 254
- BibTeX: 74
- EndNote: 150
Viewed (geographical distribution)
Total article views: 6,332 (including HTML, PDF, and XML)
Thereof 6,252 with geography defined
and 80 with unknown origin.
Total article views: 4,034 (including HTML, PDF, and XML)
Thereof 3,952 with geography defined
and 82 with unknown origin.
Total article views: 2,298 (including HTML, PDF, and XML)
Thereof 2,298 with geography defined
and 0 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
12 citations as recorded by crossref.
- Regional Soil Moisture Estimation Leveraging Multi-Source Data Fusion and Automated Machine Learning S. Li et al. https://doi.org/10.3390/rs17050837
- Development and validation of physically constrained machine learning for improving remote sensing-based evapotranspiration estimation J. Wei et al. https://doi.org/10.1016/j.rse.2026.115460
- Evaluating data-driven and hybrid modeling of terrestrial actual evapotranspiration based on an automatic machine learning approach N. Guo et al. https://doi.org/10.1016/j.jhydrol.2023.130594
- Toward an improved ensemble of multi-source daily precipitation via joint machine learning classification and regression H. Chen et al. https://doi.org/10.1016/j.atmosres.2024.107385
- A parallel evaluation framework integrating a modified SWAP model and AutoML for simulating soil salinity under mulched drip irrigation F. Gao et al. https://doi.org/10.1016/j.agwat.2026.110591
- Groundwater quality assessment using machine learning models: a comprehensive study on the industrial corridor of a semi-arid region L. Krishnamoorthy & V. Lakshmanan https://doi.org/10.1007/s11356-024-34119-7
- Data-driven total organic carbon prediction using feature selection methods incorporated in an automated machine learning framework B. Macêdo et al. https://doi.org/10.1038/s41598-025-91224-4
- Hybrid LSTM-XGBoost framework with city embeddings for CO2 emissions forecasting and scenario comparison J. Li et al. https://doi.org/10.1016/j.envres.2026.124476
- Impacts of Climatic Fluctuations and Vegetation Greening on Regional Hydrological Processes: A Case Study in the Xiaoxinganling Mountains–Sanjiang Plain Region, Northeastern China C. Xu et al. https://doi.org/10.3390/rs16152709
- AI for enhanced water quality data imputation: a deep learning perspective I. Banjara et al. https://doi.org/10.1039/D4VA00367E
- Simultaneous retrieval of hourly ground-level PM 2.5 and PM 10 from FY-4A/AGRI observations using multi-task deep learning X. Zhao et al. https://doi.org/10.1080/01431161.2026.2641831
- A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022 Q. Xu et al. https://doi.org/10.1016/j.agrformet.2025.110645
12 citations as recorded by crossref.
- Regional Soil Moisture Estimation Leveraging Multi-Source Data Fusion and Automated Machine Learning S. Li et al. https://doi.org/10.3390/rs17050837
- Development and validation of physically constrained machine learning for improving remote sensing-based evapotranspiration estimation J. Wei et al. https://doi.org/10.1016/j.rse.2026.115460
- Evaluating data-driven and hybrid modeling of terrestrial actual evapotranspiration based on an automatic machine learning approach N. Guo et al. https://doi.org/10.1016/j.jhydrol.2023.130594
- Toward an improved ensemble of multi-source daily precipitation via joint machine learning classification and regression H. Chen et al. https://doi.org/10.1016/j.atmosres.2024.107385
- A parallel evaluation framework integrating a modified SWAP model and AutoML for simulating soil salinity under mulched drip irrigation F. Gao et al. https://doi.org/10.1016/j.agwat.2026.110591
- Groundwater quality assessment using machine learning models: a comprehensive study on the industrial corridor of a semi-arid region L. Krishnamoorthy & V. Lakshmanan https://doi.org/10.1007/s11356-024-34119-7
- Data-driven total organic carbon prediction using feature selection methods incorporated in an automated machine learning framework B. Macêdo et al. https://doi.org/10.1038/s41598-025-91224-4
- Hybrid LSTM-XGBoost framework with city embeddings for CO2 emissions forecasting and scenario comparison J. Li et al. https://doi.org/10.1016/j.envres.2026.124476
- Impacts of Climatic Fluctuations and Vegetation Greening on Regional Hydrological Processes: A Case Study in the Xiaoxinganling Mountains–Sanjiang Plain Region, Northeastern China C. Xu et al. https://doi.org/10.3390/rs16152709
- AI for enhanced water quality data imputation: a deep learning perspective I. Banjara et al. https://doi.org/10.1039/D4VA00367E
- Simultaneous retrieval of hourly ground-level PM 2.5 and PM 10 from FY-4A/AGRI observations using multi-task deep learning X. Zhao et al. https://doi.org/10.1080/01431161.2026.2641831
- A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022 Q. Xu et al. https://doi.org/10.1016/j.agrformet.2025.110645
Saved (final revised paper)
Latest update: 19 Jul 2026
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.
Effectively assembling multiple models for approaching a benchmark solution remains a...
