13 citations as recorded by crossref.

1. Uncertainty Quantification of Estimated Ultimate Recovery Prediction in Shale Reservoirs Using Quantile Random Forest with Prediction Interval Calibration S. Ki et al. https://doi.org/10.32390/ksmer.2025.62.4.400
2. Multi-site learning for hydrological uncertainty prediction: the case of quantile random forests T. El Ouahabi et al. https://doi.org/10.5194/hess-30-3549-2026
3. 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. https://doi.org/10.3390/w16131904
4. Enhancing probabilistic hydrological predictions with mixture density Networks: Accounting for heteroscedasticity and Non-Gaussianity D. Li et al. https://doi.org/10.1016/j.jhydrol.2024.131737
5. Development of an integrated global sensitivity analysis strategy for evaluating process sensitivities across single- and multi-models J. Yang et al. https://doi.org/10.1016/j.jhydrol.2024.132014
6. Use of regional sensitivity analysis for diagnosing parsimony of models: A water model case study R. Srikanthan et al. https://doi.org/10.1016/j.envsoft.2025.106727
7. Improve streamflow simulations by combining machine learning pre-processing and post-processing Y. Zhang et al. https://doi.org/10.1016/j.jhydrol.2025.132904
8. Improving PM2.5 simulations using LSTM: a study on spatiotemporal generalization X. Chen et al. https://doi.org/10.1016/j.apr.2025.102647
9. Integrated Quantile Mapping and Spatial Clustering for Robust Bias Correction of Satellite Precipitation in Data-Sparse Regions G. Al-Rawas et al. https://doi.org/10.3390/su17188321
10. Physics-guided networks for probabilistic hydrodynamic forecasting in canal systems W. Liu et al. https://doi.org/10.1016/j.ese.2026.100703
11. Uncertainty Reduction in Streamflow Prediction Using Mixture Density Networks N. Kumar & T. Roy https://doi.org/10.1061/JHYEFF.HEENG-6781
12. A hybrid method coupling physical process-driven model with generative deep learning for probabilistic flood forecasting X. Yang et al. https://doi.org/10.1016/j.jhydrol.2026.135319
13. YangMPS: a new multiple-point statistics simulation method for non-stationary spatial processes J. Li et al. https://doi.org/10.1016/j.cageo.2026.106221