Articles | Volume 12, issue 5
https://doi.org/10.5194/gmd-12-1791-2019
© Author(s) 2019. 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-12-1791-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
06 May 2019
Methods for assessment of models |
| 06 May 2019
| 06 May 2019
Efficient surrogate modeling methods for large-scale Earth system models based on machine-learning techniques
Dan Lu
CORRESPONDING AUTHOR
Computational Sciences and Engineering Division, Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA
Daniel Ricciuto
Environmental Sciences Division, Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA
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- Technical note: Deep learning for creating surrogate models of precipitation in Earth system models T. Weber et al. 10.5194/acp-20-2303-2020
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- Toward Stable, General Machine‐Learned Models of the Atmospheric Chemical System M. Kelp et al. 10.1029/2020JD032759
- Evaluating Long-Term Treatment Performance and Cost of Nutrient Removal at Water Resource Recovery Facilities under Stochastic Influent Characteristics Using Artificial Neural Networks as Surrogates for Plantwide Modeling S. Li et al. 10.1021/acsestengg.1c00179
- Multi‐Objective Adaptive Surrogate Modeling‐Based Optimization for Distributed Environmental Models Based on Grid Sampling R. Sun et al. 10.1029/2020WR028740
- 100 years of data is not enough to establish reliable drought thresholds R. Link et al. 10.1016/j.hydroa.2020.100052
- Implementation of free and open-source semi-automatic feature engineering tool in landslide susceptibility mapping using the machine-learning algorithms RF, SVM, and XGBoost E. Sahin 10.1007/s00477-022-02330-y
- Bridging the gap between mechanistic biological models and machine learning surrogates I. Gherman et al. 10.1371/journal.pcbi.1010988
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20 citations as recorded by crossref.
- Can Simple Machine Learning Tools Extend and Improve Temperature-Based Methods to Infer Streambed Flux? M. Moghaddam et al. 10.3390/w13202837
- Assessing human health risk of groundwater DNAPL contamination by quantifying the model structure uncertainty Y. Pan et al. 10.1016/j.jhydrol.2020.124690
- Developing an integrated technology-environment-economics model to simulate food-energy-water systems in Corn Belt watersheds S. Li et al. 10.1016/j.envsoft.2021.105083
- Modeling Perennial Bioenergy Crops in the E3SM Land Model (ELMv2) E. Sinha et al. 10.1029/2022MS003171
- Improving E3SM Land Model Photosynthesis Parameterization via Satellite SIF, Machine Learning, and Surrogate Modeling A. Chen et al. 10.1029/2022MS003135
- A scalable framework for quantifying field-level agricultural carbon outcomes K. Guan et al. 10.1016/j.earscirev.2023.104462
- Removal of Powerline Noise in Geophysical Datasets With a Scientific Machine-Learning Based Approach J. Larsen et al. 10.1109/TGRS.2022.3223737
- Integrating plant physiology into simulation of fire behavior and effects L. Dickman et al. 10.1111/nph.18770
- Extending a land-surface model with <i>Sphagnum</i> moss to simulate responses of a northern temperate bog to whole ecosystem warming and elevated CO<sub>2</sub> X. Shi et al. 10.5194/bg-18-467-2021
- Technical note: Deep learning for creating surrogate models of precipitation in Earth system models T. Weber et al. 10.5194/acp-20-2303-2020
- Emulation of high-resolution land surface models using sparse Gaussian processes with application to JULES E. Baker et al. 10.5194/gmd-15-1913-2022
- An ultrahigh-resolution E3SM land model simulation framework and its first application to the Seward Peninsula in Alaska F. Yuan et al. 10.1016/j.jocs.2023.102145
- A Computationally Efficient, Time-Dependent Model of the Solar Wind for Use as a Surrogate to Three-Dimensional Numerical Magnetohydrodynamic Simulations M. Owens et al. 10.1007/s11207-020-01605-3
- Toward Stable, General Machine‐Learned Models of the Atmospheric Chemical System M. Kelp et al. 10.1029/2020JD032759
- Evaluating Long-Term Treatment Performance and Cost of Nutrient Removal at Water Resource Recovery Facilities under Stochastic Influent Characteristics Using Artificial Neural Networks as Surrogates for Plantwide Modeling S. Li et al. 10.1021/acsestengg.1c00179
- Multi‐Objective Adaptive Surrogate Modeling‐Based Optimization for Distributed Environmental Models Based on Grid Sampling R. Sun et al. 10.1029/2020WR028740
- 100 years of data is not enough to establish reliable drought thresholds R. Link et al. 10.1016/j.hydroa.2020.100052
- Implementation of free and open-source semi-automatic feature engineering tool in landslide susceptibility mapping using the machine-learning algorithms RF, SVM, and XGBoost E. Sahin 10.1007/s00477-022-02330-y
- Bridging the gap between mechanistic biological models and machine learning surrogates I. Gherman et al. 10.1371/journal.pcbi.1010988
- A surrogate modeling method for distributed land surface hydrological models based on deep learning R. Sun et al. 10.1016/j.jhydrol.2023.129944
Latest update: 10 Dec 2023
Short summary
This work uses machine-learning techniques to advance the predictive understanding of large-scale Earth systems.
This work uses machine-learning techniques to advance the predictive understanding of...
