42 citations as recorded by crossref.

1. Can Simple Machine Learning Tools Extend and Improve Temperature-Based Methods to Infer Streambed Flux? M. Moghaddam et al. https://doi.org/10.3390/w13202837
2. Modeling Perennial Bioenergy Crops in the E3SM Land Model (ELMv2) E. Sinha et al. https://doi.org/10.1029/2022MS003171
3. Improving E3SM Land Model Photosynthesis Parameterization via Satellite SIF, Machine Learning, and Surrogate Modeling A. Chen et al. https://doi.org/10.1029/2022MS003135
4. Artificial intelligence for geoscience: Progress, challenges, and perspectives T. Zhao et al. https://doi.org/10.1016/j.xinn.2024.100691
5. A model-data fusion approach for quantifying the carbon budget in cotton agroecosystems across the United States R. Qin et al. https://doi.org/10.1016/j.agrformet.2025.110407
6. GenAI4UQ: A software for forward and inverse uncertainty quantification using conditional generative AI M. Fan et al. https://doi.org/10.1016/j.softx.2025.102232
7. Integrating plant physiology into simulation of fire behavior and effects L. Dickman et al. https://doi.org/10.1111/nph.18770
8. Machine learning emulators of dynamical systems for understanding ecosystem behaviour O. Pomarol Moya et al. https://doi.org/10.1016/j.ecolmodel.2024.110956
9. Assessing and enhancing Noah-MP land surface modeling over tropical forests using machine learning techniques Y. Cheng et al. https://doi.org/10.5194/gmd-19-2197-2026
10. Emulator-based calibration of a dynamic grassland model using recurrent neural networks and Hamiltonian Monte Carlo V. Aakula et al. https://doi.org/10.1016/j.eja.2025.127769
11. 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. https://doi.org/10.1007/s11207-020-01605-3
12. Application of Deep Learning Methodologies in Steel Wheel Design D. Ronco et al. https://doi.org/10.1155/mdp2/1430406
13. Angle information enhanced kriging-assisted adaptive evolutionary algorithm for computationally highly expensive high-dimensional problems H. Lu et al. https://doi.org/10.1016/j.swevo.2025.101992
14. Surrogate construction via weight parameterization of residual neural networks O. Diaz-Ibarra et al. https://doi.org/10.1016/j.cma.2024.117468
15. Assessing human health risk of groundwater DNAPL contamination by quantifying the model structure uncertainty Y. Pan et al. https://doi.org/10.1016/j.jhydrol.2020.124690
16. Developing an integrated technology-environment-economics model to simulate food-energy-water systems in Corn Belt watersheds S. Li et al. https://doi.org/10.1016/j.envsoft.2021.105083
17. Toward real-time high-resolution fluvial flood forecasting: A robust surrogate approach based on overland flow models G. Nguyen et al. https://doi.org/10.1016/j.envsoft.2025.106716
18. A scalable framework for quantifying field-level agricultural carbon outcomes K. Guan et al. https://doi.org/10.1016/j.earscirev.2023.104462
19. Hector V3.2.0: functionality and performance of a reduced-complexity climate model K. Dorheim et al. https://doi.org/10.5194/gmd-17-4855-2024
20. Removal of Powerline Noise in Geophysical Datasets With a Scientific Machine-Learning Based Approach J. Larsen et al. https://doi.org/10.1109/TGRS.2022.3223737
21. Developing Guidelines for working with Multi-Model Ensembles in CMIP A. Katzenberger et al. https://doi.org/10.5194/esd-17-495-2026
22. Machine learning-based surrogate modelling of a robust, sustainable development goal (SDG)-compliant land-use future for Australia at high spatial resolution M. Khan et al. https://doi.org/10.1016/j.jenvman.2024.121296
23. Exploring the potential of history matching for land surface model calibration N. Raoult et al. https://doi.org/10.5194/gmd-17-5779-2024
24. Extending a land-surface model with Sphagnum moss to simulate responses of a northern temperate bog to whole ecosystem warming and elevated CO2 X. Shi et al. https://doi.org/10.5194/bg-18-467-2021
25. Technical note: Deep learning for creating surrogate models of precipitation in Earth system models T. Weber et al. https://doi.org/10.5194/acp-20-2303-2020
26. RTM Surrogate Modeling in Optical Remote Sensing: A Review of Emulation for Vegetation and Atmosphere Applications J. Verrelst et al. https://doi.org/10.3390/rs17213618
27. Emulation of high-resolution land surface models using sparse Gaussian processes with application to JULES E. Baker et al. https://doi.org/10.5194/gmd-15-1913-2022
28. Quantifying uncertainty in simulations of the West African monsoon with the use of surrogate models M. Fischer et al. https://doi.org/10.5194/wcd-5-511-2024
29. A tree location optimization via metamodel-based simulation framework for mitigating extreme heat in communities Y. Bai et al. https://doi.org/10.1016/j.compenvurbsys.2026.102438
30. GALDS: A graph-autoencoder-based latent dynamics surrogate model to predict neurite material transport T. Hsieh & Y. Zhang https://doi.org/10.1016/j.cma.2025.118409
31. Predicting Concentrations of PM2.5, PM10, CO, VOC, and NOx on the Urban Scale Using Machine Learning-Based Surrogate Models P. Lewicki et al. https://doi.org/10.3390/app16010334
32. An ultrahigh-resolution E3SM land model simulation framework and its first application to the Seward Peninsula in Alaska F. Yuan et al. https://doi.org/10.1016/j.jocs.2023.102145
33. Using Machine Learning for Climate Modelling: Application of Neural Networks to a Slow-Fast Chaotic Dynamical System as a Case Study S. Soldatenko & Y. Angudovich https://doi.org/10.3390/cli12110189
34. Toward Stable, General Machine‐Learned Models of the Atmospheric Chemical System M. Kelp et al. https://doi.org/10.1029/2020JD032759
35. 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. https://doi.org/10.1021/acsestengg.1c00179
36. Multi‐Objective Adaptive Surrogate Modeling‐Based Optimization for Distributed Environmental Models Based on Grid Sampling R. Sun et al. https://doi.org/10.1029/2020WR028740
37. PathLines transformation for adaptive additional sampling in surrogate-based uncertainty quantification applied to high-altitude drone rotor performance analysis A. Boksawat et al. https://doi.org/10.1016/j.rineng.2026.111023
38. 100 years of data is not enough to establish reliable drought thresholds R. Link et al. https://doi.org/10.1016/j.hydroa.2020.100052
39. 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 https://doi.org/10.1007/s00477-022-02330-y
40. Bridging the gap between mechanistic biological models and machine learning surrogates I. Gherman et al. https://doi.org/10.1371/journal.pcbi.1010988
41. A numerical compass for experiment design in chemical kinetics and molecular property estimation M. Krüger et al. https://doi.org/10.1186/s13321-024-00825-0
42. A surrogate modeling method for distributed land surface hydrological models based on deep learning R. Sun et al. https://doi.org/10.1016/j.jhydrol.2023.129944