16 citations as recorded by crossref.

1. A Century of Data: Machine Learning Approaches to Drought Prediction and Trend Analysis in Arid Regions M. Bouaziz et al. https://doi.org/10.3390/w17243567
2. Improving PDSI Z-Index Prediction with Ensemble Learning: A Case Study from the Troy Region of Türkiye U. Mucan & E. Arslantaş Civelekoğlu https://doi.org/10.3390/su18041752
3. Machine and Deep Learning Approaches for Drought Characterization and Prediction: a Comprehensive Review C. Naresh & A. Mathew https://doi.org/10.1007/s10666-026-10131-8
4. A XGBoost-based composite drought index combining multi-source remote sensing data for drought monitoring in China X. Huang et al. https://doi.org/10.1016/j.ejrh.2026.103623
5. A hybrid TFN–LSTM model for groundwater level forecasting in Rhode Island, USA H. Chu et al. https://doi.org/10.1016/j.jenvman.2026.130474
6. Comparative Assessment of Machine Learning and Statistical Models for Precipitation Forecasting in Semi-Arid Regions K. Eryürük & Ş. Eryürük https://doi.org/10.64808/engineeringperspective.1914439
7. An Adaptive Grey Wolf Optimized Bidirectional LSTM Framework for Flood Risk-Oriented Rainfall Forecasting in Tropical Climate Systems Y. Sari et al. https://doi.org/10.48084/etasr.18161
8. A comparative assessment of cluster-based regionalization approaches using conceptual rainfall–runoff models J. Ougahi & J. Rowan https://doi.org/10.1038/s41598-026-49424-z
9. New multivariate composite remote sensing drought index based on machine learning and geospatial techniques, insights from Northern Iraq K. Qaraghuli et al. https://doi.org/10.1016/j.ejrh.2026.103211
10. A Novel Integration of Decision Tree and K-Means for Enhanced Monthly Rainfall Forecasting and Variable Importance Ranking S. Mirhashemi & M. Karimi https://doi.org/10.1007/s11269-026-04532-3
11. GeoAI-Based multi-model framework for spatiotemporal drought modeling in arid environments A. Alruzuq https://doi.org/10.1080/01431161.2026.2689555
12. Disentangling Climatic and Anthropogenic Drivers of Vegetation Dynamics in the Upper Indus Basin Using Multi-Source Remote Sensing K. Ahmad et al. https://doi.org/10.3390/w18121451
13. Soil moisture measurements: a review E. Eishoeei et al. https://doi.org/10.1016/j.compag.2025.111379
14. Dual-Track Attribution and Compound-Drought Risk Quantification Under Coupled Climate Change and Land-Use Dynamics: An Integrated SWAT–CMIP6–Budyko–XGBoost/SHAP–C-Vine Copula Framework Applied to a Subtropical Monsoon Basin J. Wang et al. https://doi.org/10.3390/agriculture16111178
15. Predicción de sequías en zonas altoandinas de Puno mediante un modelo híbrido LSTM–XGBOOST K. Colquehuanca Zapana https://doi.org/10.57166/riqchary/v8.n1.2026.7
16. Meteorological-to-hydrological drought propagation and its drivers in the Upper Indus Basin, Pakistan: A multiscale analysis using explainable machine learning K. Ahmad et al. https://doi.org/10.1016/j.ejrh.2026.103751