22 citations as recorded by crossref.

1. Convergence of mechanistic modeling and artificial intelligence in hydrologic science and engineering R. Muñoz-Carpena et al. 10.1371/journal.pwat.0000059
2. Enhancing Streamflow Prediction Physically Consistently Using Process-Based Modeling and Domain Knowledge: A Review B. Yifru et al. 10.3390/su16041376
3. On the need for physical constraints in deep learning rainfall–runoff projections under climate change: a sensitivity analysis to warming and shifts in potential evapotranspiration S. Wi & S. Steinschneider 10.5194/hess-28-479-2024
4. Transfer learning framework for streamflow prediction in large-scale transboundary catchments: Sensitivity analysis and applicability in data-scarce basins K. Ma et al. 10.1007/s11442-024-2235-x
5. Metamorphic testing of machine learning and conceptual hydrologic models P. Reichert et al. 10.5194/hess-28-2505-2024
6. Assessment of Time Series Models for Mean Discharge Modeling and Forecasting in a Sub-Basin of the Paranaíba River, Brazil G. Costa et al. 10.3390/hydrology10110208
7. Accelerating hydrodynamic simulations of urban drainage systems with physics-guided machine learning R. Palmitessa et al. 10.1016/j.watres.2022.118972
8. Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States K. Hunt et al. 10.5194/hess-26-5449-2022
9. To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization E. Acuña Espinoza et al. 10.5194/hess-28-2705-2024
10. A process-driven deep learning hydrological model for daily rainfall-runoff simulation H. Li et al. 10.1016/j.jhydrol.2024.131434
11. Enhancing process-based hydrological models with embedded neural networks: A hybrid approach B. Li et al. 10.1016/j.jhydrol.2023.130107
12. A comparative evaluation of streamflow prediction using the SWAT and NNAR models in the Meenachil River Basin of Central Kerala, India M. Saranya & V. Vinish 10.2166/wst.2023.330
13. CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland M. Höge et al. 10.5194/essd-15-5755-2023
14. Long-term streamflow forecasting in data-scarce regions: Insightful investigation for leveraging satellite-derived data, Informer architecture, and concurrent fine-tuning transfer learning F. Ghobadi et al. 10.1016/j.jhydrol.2024.130772
15. Assessment of monthly runoff simulations based on a physics-informed machine learning framework: The effect of intermediate variables in its construction C. Deng et al. 10.1016/j.jenvman.2024.121299
16. Short-term runoff forecasting in an alpine catchment with a long short-term memory neural network C. Frank et al. 10.3389/frwa.2023.1126310
17. DeepGR4J: A deep learning hybridization approach for conceptual rainfall-runoff modelling A. Kapoor et al. 10.1016/j.envsoft.2023.105831
18. Validation and parameterization of a novel physics-constrained neural dynamics model applied to turbulent fluid flow V. Shankar et al. 10.1063/5.0122115
19. Deep learning for water quality W. Zhi et al. 10.1038/s44221-024-00202-z
20. Differentiable modelling to unify machine learning and physical models for geosciences C. Shen et al. 10.1038/s43017-023-00450-9
21. A fast physically-guided emulator of MATSIRO land surface model R. Olson et al. 10.1016/j.jhydrol.2024.131093
22. Long‐Lead Drought Forecasting Across the Continental United States Using Burg Entropy Spectral Analysis With a Multiresolution Approach J. Han & V. Singh 10.1029/2022WR034188