Articles | Volume 14, issue 8
https://doi.org/10.5194/gmd-14-5205-2021
© Author(s) 2021. 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-14-5205-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
18 Aug 2021
Development and technical paper |
| 18 Aug 2021
| 18 Aug 2021
Copula-based synthetic data augmentation for machine-learning emulators
David Meyer
CORRESPONDING AUTHOR
Department of Meteorology, University of Reading, Reading, UK
Department of Civil and Environmental Engineering, Imperial College
London, London, UK
Thomas Nagler
Mathematical Institute, Leiden University, Leiden,
the Netherlands
Robin J. Hogan
European Centre for Medium-Range Weather Forecasts,
Reading, UK
Department of Meteorology, University of Reading, Reading, UK
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Cited
17 citations as recorded by crossref.
- Machine Learning Emulation of 3D Cloud Radiative Effects D. Meyer et al. 10.1029/2021MS002550
- Machine Learning Emulation of Urban Land Surface Processes D. Meyer et al. 10.1029/2021MS002744
- Challenges and Benchmark Datasets for Machine Learning in the Atmospheric Sciences: Definition, Status, and Outlook P. Dueben et al. 10.1175/AIES-D-21-0002.1
- Statistical mechanics in climate emulation: Challenges and perspectives I. Sudakow et al. 10.1017/eds.2022.15
- Human-in-the-Loop Digital Twin Framework for Ergonomics of Exoskeletons in Construction A. Afolabi et al. 10.36680/j.itcon.2024.048
- Integrative modeling of heterogeneous soil salinity using sparse ground samples and remote sensing images L. Wang et al. 10.1016/j.geoderma.2022.116321
- Synthia: multidimensional synthetic data generation in Python D. Meyer & T. Nagler 10.21105/joss.02863
- Improving Predictions of Technical Inefficiency R. James et al. 10.2139/ssrn.4028125
- Yet Another Discriminant Analysis (YADA): A Probabilistic Model for Machine Learning Applications R. Field et al. 10.3390/math12213392
- Improving Weather Forecasts for Sailing Events Using a Combination of a Numerical Forecast Model and Machine Learning Postprocessing S. Beimel et al. 10.3390/app14072950
- Integrative Stacking Machine Learning Model for Small Cell Lung Cancer Prediction Using Metabolomics Profiling M. Sumon et al. 10.3390/cancers16244225
- Soybean yield prediction using machine learning algorithms under a cover crop management system L. Santos et al. 10.1016/j.atech.2024.100442
- A novel classical machine learning framework for early sepsis prediction using electronic health record data from ICU patients J. Prithula et al. 10.1016/j.compbiomed.2024.109284
- A framework to create, evaluate and select synthetic datasets for survival prediction in oncology A. Christoforou et al. 10.1016/j.compbiomed.2025.110198
- Synthetic data generation using Copula model and driving behavior analysis E. Savran & F. Karpat 10.1016/j.asej.2024.103060
- Generative Adversarial Networks for Synthetic Data Generation in Finance: Evaluating Statistical Similarities and Quality Assessment F. Ramzan et al. 10.3390/ai5020035
- Comparison of tabular synthetic data generation techniques using propensity and cluster log metric A. Pathare et al. 10.1016/j.jjimei.2023.100177
15 citations as recorded by crossref.
- Machine Learning Emulation of 3D Cloud Radiative Effects D. Meyer et al. 10.1029/2021MS002550
- Machine Learning Emulation of Urban Land Surface Processes D. Meyer et al. 10.1029/2021MS002744
- Challenges and Benchmark Datasets for Machine Learning in the Atmospheric Sciences: Definition, Status, and Outlook P. Dueben et al. 10.1175/AIES-D-21-0002.1
- Statistical mechanics in climate emulation: Challenges and perspectives I. Sudakow et al. 10.1017/eds.2022.15
- Human-in-the-Loop Digital Twin Framework for Ergonomics of Exoskeletons in Construction A. Afolabi et al. 10.36680/j.itcon.2024.048
- Integrative modeling of heterogeneous soil salinity using sparse ground samples and remote sensing images L. Wang et al. 10.1016/j.geoderma.2022.116321
- Synthia: multidimensional synthetic data generation in Python D. Meyer & T. Nagler 10.21105/joss.02863
- Improving Predictions of Technical Inefficiency R. James et al. 10.2139/ssrn.4028125
- Yet Another Discriminant Analysis (YADA): A Probabilistic Model for Machine Learning Applications R. Field et al. 10.3390/math12213392
- Improving Weather Forecasts for Sailing Events Using a Combination of a Numerical Forecast Model and Machine Learning Postprocessing S. Beimel et al. 10.3390/app14072950
- Integrative Stacking Machine Learning Model for Small Cell Lung Cancer Prediction Using Metabolomics Profiling M. Sumon et al. 10.3390/cancers16244225
- Soybean yield prediction using machine learning algorithms under a cover crop management system L. Santos et al. 10.1016/j.atech.2024.100442
- A novel classical machine learning framework for early sepsis prediction using electronic health record data from ICU patients J. Prithula et al. 10.1016/j.compbiomed.2024.109284
- A framework to create, evaluate and select synthetic datasets for survival prediction in oncology A. Christoforou et al. 10.1016/j.compbiomed.2025.110198
- Synthetic data generation using Copula model and driving behavior analysis E. Savran & F. Karpat 10.1016/j.asej.2024.103060
2 citations as recorded by crossref.
- Generative Adversarial Networks for Synthetic Data Generation in Finance: Evaluating Statistical Similarities and Quality Assessment F. Ramzan et al. 10.3390/ai5020035
- Comparison of tabular synthetic data generation techniques using propensity and cluster log metric A. Pathare et al. 10.1016/j.jjimei.2023.100177
Latest update: 06 May 2025
Short summary
A major limitation in training machine-learning emulators is often caused by the lack of data. This paper presents a cheap way to increase the size of training datasets using statistical techniques and thereby improve the performance of machine-learning emulators.
A major limitation in training machine-learning emulators is often caused by the lack of data....
