13 citations as recorded by crossref.

1. 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
2. Proper Weather Forecasting Internet of Things Sensor Framework with Machine Learning A. Turukmane & S. Pande 10.4108/eetiot.5382
3. Representing chemical history in ozone time-series predictions – a model experiment study building on the MLAir (v1.5) deep learning framework F. Kleinert et al. 10.5194/gmd-15-8913-2022
4. Exploring the potential of machine learning for simulations of urban ozone variability N. Ojha et al. 10.1038/s41598-021-01824-z
5. Explainable Machine Learning Reveals Capabilities, Redundancy, and Limitations of a Geospatial Air Quality Benchmark Dataset S. Stadtler et al. 10.3390/make4010008
6. 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
7. 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
8. Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties C. Betancourt et al. 10.5194/gmd-15-4331-2022
9. Feature selection for global tropospheric ozone prediction based on the BO-XGBoost-RFE algorithm B. Zhang et al. 10.1038/s41598-022-13498-2
10. Graph Machine Learning for Improved Imputation of Missing Tropospheric Ozone Data C. Betancourt et al. 10.1021/acs.est.3c05104
11. 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
12. 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
13. Deep Learning Approach for Assessing Air Quality During COVID-19 Lockdown in Quito P. Chau et al. 10.3389/fdata.2022.842455