43 citations as recorded by crossref.

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5. Evaluating Performance of Multiple Machine Learning Models for Drought Monitoring: A Case Study of Typical Grassland in Inner Mongolia Y. Wang et al. https://doi.org/10.3390/land13060754
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7. Utilizing machine learning and CMIP6 projections for short-term agricultural drought monitoring in central Europe (1900–2100) S. Mohammed et al. https://doi.org/10.1016/j.jhydrol.2024.130968
8. Artificial intelligence for climate prediction of extremes: State of the art, challenges, and future perspectives S. Materia et al. https://doi.org/10.1002/wcc.914
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13. Evaluation of Tropical Cyclone Disaster Loss Using Machine Learning Algorithms with an eXplainable Artificial Intelligence Approach S. Liu et al. https://doi.org/10.3390/su151612261
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23. Preface: Recent advances in drought and water scarcity monitoring, modelling, and forecasting B. Bonaccorso et al. https://doi.org/10.5194/nhess-22-1857-2022
24. A Contemporary Review on Deep Learning Models for Drought Prediction A. Gyaneshwar et al. https://doi.org/10.3390/su15076160
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28. Assessment of Multiple Machine Learning Models for Critical Mineral Exploration Data Quality in the Brazilian Shield H. Barbosa https://doi.org/10.3390/app16136658
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30. Improving meteorological drought simulation in Iran using wavelet-enhanced deep learning models M. Heydarizad et al. https://doi.org/10.1007/s00704-025-05525-9
31. Interpretability of compound drought-hot extreme index prediction model: a regional study in Iran M. Moghaddasi et al. https://doi.org/10.1007/s11356-025-36257-y
32. Projection and assessment of future droughts in Iowa: developing a machine learning model and an interactive application I. Cintura & A. Arenas https://doi.org/10.3389/fenvs.2025.1564670
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39. Comparative Assessment of Random Forest and Linear Regression for Predicting South Asian Aridity Driven by Tropical Ocean Signals G. Ebaju et al. https://doi.org/10.3390/cli14090200
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41. Unlocking drought in pistachio orchards: monitoring and forecasting using landsat time series and machine learning techniques N. Zarei et al. https://doi.org/10.1007/s13580-025-00689-9
42. The Drought Regime in Southern Africa: A Systematic Review F. Chivangulula et al. https://doi.org/10.3390/cli11070147
43. Could climate change exacerbate droughts in Bangladesh in the future? M. Rahman et al. https://doi.org/10.1016/j.jhydrol.2023.130096