14 citations as recorded by crossref.

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3. Machine learning meta-models for fast parameter identification of the lattice discrete particle model Y. Lyu et al. 10.1007/s00466-023-02320-z
4. Pollution loads in the middle-lower Yangtze river by coupling water quality models with machine learning S. Huang et al. 10.1016/j.watres.2024.122191
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6. A stochastic conceptual-data-driven approach for improved hydrological simulations J. Quilty et al. 10.1016/j.envsoft.2022.105326
7. Feature importance measures to dissect the role of sub-basins in shaping the catchment hydrological response: a proof of concept F. Cappelli et al. 10.1007/s00477-022-02332-w
8. Integrated model for the fast assessment of flood volume: Modelling – management, uncertainty and sensitivity analysis B. Szeląg et al. 10.1016/j.jhydrol.2023.129967
9. A Review on Snowmelt Models: Progress and Prospect G. Zhou et al. 10.3390/su132011485
10. Coupling Machine Learning Into Hydrodynamic Models to Improve River Modeling With Complex Boundary Conditions S. Huang et al. 10.1029/2022WR032183
11. 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
12. Enhancing root-zone soil moisture estimation using Richards' equation and dynamic surface soil moisture data X. Zha et al. 10.1016/j.agwat.2025.109460
13. A review of deep learning and machine learning techniques for hydrological inflow forecasting S. Latif & A. Ahmed 10.1007/s10668-023-03131-1
14. An Example of Augmenting Regional Sensitivity Analysis Using Machine Learning Software R. Spear et al. 10.1029/2019WR026379