6 citations as recorded by crossref.

1. Advancing Hydrology through Machine Learning: Insights, Challenges, and Future Directions Using the CAMELS, Caravan, GRDC, CHIRPS, PERSIANN, NLDAS, GLDAS, and GRACE Datasets F. Hasan et al. 10.3390/w16131904
2. Enhancing probabilistic hydrological predictions with mixture density Networks: Accounting for heteroscedasticity and Non-Gaussianity D. Li et al. 10.1016/j.jhydrol.2024.131737
3. Development of an integrated global sensitivity analysis strategy for evaluating process sensitivities across single- and multi-models J. Yang et al. 10.1016/j.jhydrol.2024.132014
4. Improve streamflow simulations by combining machine learning pre-processing and post-processing Y. Zhang et al. 10.1016/j.jhydrol.2025.132904
5. A stochastic deep-learning-based approach for improved streamflow simulation N. Dolatabadi & B. Zahraie 10.1007/s00477-023-02567-1
6. Comparing quantile regression forest and mixture density long short-term memory models for probabilistic post-processing of satellite precipitation-driven streamflow simulations Y. Zhang et al. 10.5194/hess-27-4529-2023