17 citations as recorded by crossref.

1. Estimation of Planetary Boundary Layer Height From Lidar by Combining Gradient Method and Machine Learning Algorithms H. Li et al. https://doi.org/10.1109/TGRS.2023.3329122
2. Enhancing 100 m wind speed forecasts in China based on CatBoost feature selection and stacking ensemble learning C. Jin et al. https://doi.org/10.1007/s00704-026-06288-7
3. Exogenous atmospheric variables as wind speed predictors in machine learning A. Dalton & B. Bekker https://doi.org/10.1016/j.apenergy.2022.119257
4. Explainable Machine Learning (XML) to predict external wind pressure of a low-rise building in urban-like settings D. Meddage et al. https://doi.org/10.1016/j.jweia.2022.105027
5. Enabling Virtual Met Masts for wind energy applications through machine learning-methods S. Schwegmann et al. https://doi.org/10.1016/j.egyai.2022.100209
6. Towards Sustainable Urban Mobility: Leveraging Machine Learning Methods for QA of Meteorological Measurements in the Urban Area D. Sládek et al. https://doi.org/10.3390/su16135713
7. A review of physics-based machine learning in civil engineering S. Vadyala et al. https://doi.org/10.1016/j.rineng.2021.100316
8. A multi-dimensional interpretable wind speed forecasting model with two-stage feature exploring B. Wu et al. https://doi.org/10.1016/j.renene.2025.124028
9. Physics-data dual-driven approach for structural health monitoring: A systematic review W. Long et al. https://doi.org/10.1016/j.eswa.2026.132038
10. Assessment of hybrid machine learning algorithms using TRMM rainfall data for daily inflow forecasting in Três Marias Reservoir, eastern Brazil E. Gomaa et al. https://doi.org/10.1016/j.heliyon.2023.e18819
11. Research on adaptive combined wind speed prediction for each season based on improved gray relational analysis Y. Zhu https://doi.org/10.1007/s11356-022-22957-2
12. Using Trees as a Natural Weather Station for Wind Pattern Forecasting Applied to Forest Firefighting V. Aires et al. https://doi.org/10.3390/s25237205
13. Estimating the urban atmospheric boundary layer height from remote sensing applying machine learning techniques G. de Arruda Moreira et al. https://doi.org/10.1016/j.atmosres.2021.105962
14. Fusing physics-inferred information from stochastic model with machine learning approaches for degradation prediction Z. Li et al. https://doi.org/10.1016/j.ress.2022.109078
15. On the estimation of boundary layer heights: a machine learning approach R. Krishnamurthy et al. https://doi.org/10.5194/amt-14-4403-2021
16. Improving Surface Wind Speed Forecasts Using an Offline Surface Multilayer Model With Optimal Ground Forcing J. Feng et al. https://doi.org/10.1029/2022MS003072
17. A Comprehensive Degradation Modeling Comparison From Statistical to Artificial Intelligence Models for Curing Oven Chains H. Misaii et al. https://doi.org/10.1002/asmb.2930