14 citations as recorded by crossref.

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6. Explainable Machine Learning Reveals Capabilities, Redundancy, and Limitations of a Geospatial Air Quality Benchmark Dataset S. Stadtler et al. 10.3390/make4010008
7. Improving rainfall forecast at the district scale over the eastern Indian region using deep neural network D. Trivedi et al. 10.1007/s00704-023-04734-4
8. Importance of ozone precursors information in modelling urban surface ozone variability using machine learning algorithm V. Balamurugan et al. 10.1038/s41598-022-09619-6
9. Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties C. Betancourt et al. 10.5194/gmd-15-4331-2022
10. Feature selection for global tropospheric ozone prediction based on the BO-XGBoost-RFE algorithm B. Zhang et al. 10.1038/s41598-022-13498-2
11. Graph Machine Learning for Improved Imputation of Missing Tropospheric Ozone Data C. Betancourt et al. 10.1021/acs.est.3c05104
12. Interactions between atmospheric composition and climate change – progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP S. Fiedler et al. 10.5194/gmd-17-2387-2024
13. LandBench 1.0: A benchmark dataset and evaluation metrics for data-driven land surface variables prediction Q. Li et al. 10.1016/j.eswa.2023.122917
14. Deep Learning Approach for Assessing Air Quality During COVID-19 Lockdown in Quito P. Chau et al. 10.3389/fdata.2022.842455