16 citations as recorded by crossref.

1. A generalized approach for predicting and mitigating total dissolved gas supersaturation at data-scarce dams S. Li et al. https://doi.org/10.1016/j.watres.2025.125282
2. Balancing Results from AI-Based Geostatistics versus Fuzzy Inference by Game Theory Bargaining to Improve a Groundwater Monitoring Network M. Hashemi et al. https://doi.org/10.3390/make6030092
3. Bridging data scarcity in groundwater quality studies: A systematic evaluation of statistical and deep learning-based generators C. Aju et al. https://doi.org/10.1016/j.pce.2026.104327
4. Feasibility Screening of River Basin Management Plan Delivery Under War-Driven Uncertainty: The Ukrainian Tisza Sub-Basin (2025–2030) S. Lousada et al. https://doi.org/10.3390/w18101178
5. Reconstruction of Sparse Stream Flow and Concentration Time‐Series Through Compressed Sensing K. Zhang et al. https://doi.org/10.1029/2022GL101177
6. A novel groundwater monitoring network design framework for long-term and economical data monitoring S. Jena https://doi.org/10.1016/j.gsd.2024.101252
7. Estimating Regional Groundwater Level by Combining Satellite, Model, and Large-Sample Observations Inputs Y. Cao et al. https://doi.org/10.3390/rs18101622
8. Streamflow Prediction in Poorly Gauged Watersheds in the United States Through Data‐Driven Sparse Sensing K. Zhang et al. https://doi.org/10.1029/2022WR034092
9. A machine learning approach to site groundwater contamination monitoring wells V. Gómez-Escalonilla et al. https://doi.org/10.1007/s13201-024-02320-1
10. Spatiotemporal prediction for groundwater heavy metal contamination using Soft-DTW-based clustering and graph neural network framework Y. He et al. https://doi.org/10.1016/j.watres.2025.125245
11. Surrogate modeling for rapid estimation of spatially-resolved flood damage: Application to a coastal region S. Li et al. https://doi.org/10.1016/j.jhydrol.2025.134763
12. Climate-hydrology-topography-anthropogenic factors jointly drive the evolution of vegetation coverage in semi-arid regions: A downscaling approach based on random forest and nonlinear residual correction J. Sun et al. https://doi.org/10.1016/j.eiar.2026.108457
13. A digital ecohydraulic twin for riverine habitat prediction and optimization S. Li et al. https://doi.org/10.1016/j.jenvman.2025.128445
14. Agricultural and energy sectors dominate Irans water crisis: A GRACE-GLDAS quantification (2002_2023) X. Yuan et al. https://doi.org/10.13168/AGG.2025.0027
15. NiMo 4.0 – Enabling advanced data analytics with AI for environmental governance in the water domain M. Budde et al. https://doi.org/10.1515/auto-2024-0034
16. Data-driven insights into urban groundwater contamination: A framework for SDG 6 implementation in developing countries M. Hamza et al. https://doi.org/10.1016/j.clwas.2025.100351