16 citations as recorded by crossref.

1. Machine learning meta-models for fast parameter identification of the lattice discrete particle model Y. Lyu et al.
2. Pollution loads in the middle-lower Yangtze river by coupling water quality models with machine learning S. Huang et al.
3. Coupling Machine Learning Into Hydrodynamic Models to Improve River Modeling With Complex Boundary Conditions S. Huang et al.
4. Enhancing root-zone soil moisture estimation using Richards' equation and dynamic surface soil moisture data X. Zha et al.
5. Shyft v4.8: a framework for uncertainty assessment and distributed hydrologic modeling for operational hydrology J. Burkhart et al.
6. A novel ensemble-based conceptual-data-driven approach for improved streamflow simulations A. Sikorska-Senoner & J. Quilty
7. Rivers in transition: 21st-century hydroclimate projections for the Thaya River Basin, Central Europe J. Bernsteinová et al.
8. Root zone soil moisture estimation with Random Forest C. Carranza et al.
9. A stochastic conceptual-data-driven approach for improved hydrological simulations J. Quilty et al.
10. Feature importance measures to dissect the role of sub-basins in shaping the catchment hydrological response: a proof of concept F. Cappelli et al.
11. Integrated model for the fast assessment of flood volume: Modelling – management, uncertainty and sensitivity analysis B. Szeląg et al.
12. A Review on Snowmelt Models: Progress and Prospect G. Zhou et al.
13. How to out-perform default random forest regression: choosing hyperparameters for applications in large-sample hydrology D. Bilolikar et al.
14. Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States K. Hunt et al.
15. TeaNet: An Enhanced Attention Network for Climate-Resilient River Discharge Forecasting Under CMIP6 SSP585 Projections P. Parasar et al.
16. A hybrid process-data driven framework for real-time hydrological forecasting with interpretable deep learning F. Zhu et al.