12 citations as recorded by crossref.

1. A fusion-based data assimilation framework for runoff prediction considering multiple sources of precipitation M. Bahrami et al. 10.1080/02626667.2023.2180375
2. Integrating artificial intelligence and machine learning in hydrological modeling for sustainable resource management S. Marshall et al. 10.1080/15715124.2025.2478280
3. Leveraging a novel hybrid ensemble and optimal interpolation approach for enhanced streamflow and flood prediction M. El Gharamti et al. 10.5194/hess-28-3133-2024
4. Flood Prediction and Uncertainty Estimation Using Deep Learning V. Gude et al. 10.3390/w12030884
5. Combining Models of Root-Zone Hydrology and Geoelectrical Measurements: Recent Advances and Future Prospects B. Mary et al. 10.3389/frwa.2021.767910
6. The Quest for Model Uncertainty Quantification: A Hybrid Ensemble and Variational Data Assimilation Framework P. Abbaszadeh et al. 10.1029/2018WR023629
7. A Calibration Framework for High‐Resolution Hydrological Models Using a Multiresolution and Heterogeneous Strategy R. Sun et al. 10.1029/2019WR026541
8. Application of Parameter Screening to Derive Optimal Initial State Adjustments for Streamflow Forecasting J. Mai et al. 10.1029/2020WR027960
9. Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends C. Gonzales-Inca et al. 10.3390/w14142211
10. Deep‐Learning‐Based Adjoint State Method: Methodology and Preliminary Application to Inverse Modeling C. Xiao et al. 10.1029/2020WR027400
11. Towards reliable uncertainty quantification for hydrologic predictions, Part I: Development of a particle copula Metropolis Hastings method Y. Fan et al. 10.1016/j.jhydrol.2022.128163
12. A Bayesian-variational cyclic method for solving estimation problems characterized by non-uniqueness (equifinality) I. Gejadze et al. 10.1016/j.jcp.2023.112239