33 citations as recorded by crossref.

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10. Angle information enhanced kriging-assisted adaptive evolutionary algorithm for computationally highly expensive high-dimensional problems H. Lu et al. 10.1016/j.swevo.2025.101992
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13. 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
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16. Removal of Powerline Noise in Geophysical Datasets With a Scientific Machine-Learning Based Approach J. Larsen et al. 10.1109/TGRS.2022.3223737
17. 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. 10.1016/j.jenvman.2024.121296
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19. 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. 10.5194/bg-18-467-2021
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22. Quantifying uncertainty in simulations of the West African monsoon with the use of surrogate models M. Fischer et al. 10.5194/wcd-5-511-2024
23. 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
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25. Toward Stable, General Machine‐Learned Models of the Atmospheric Chemical System M. Kelp et al. 10.1029/2020JD032759
26. 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
27. Multi‐Objective Adaptive Surrogate Modeling‐Based Optimization for Distributed Environmental Models Based on Grid Sampling R. Sun et al. 10.1029/2020WR028740
28. 100 years of data is not enough to establish reliable drought thresholds R. Link et al. 10.1016/j.hydroa.2020.100052
29. 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
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