14 citations as recorded by crossref.

1. Forecasting bathing water quality in the UK: A critical review K. Krupska et al. https://doi.org/10.1002/wat2.1718
2. SSPP: a Novel Flood Probabilistic Forecasting Model Based on Synergistic Seq2Seq Framework and Peak-Enhanced Loss Function C. Chen et al. https://doi.org/10.1007/s11269-025-04478-y
3. A deep learning network for improving predictions of maximum and minimum temperatures over complex terrain L. Xu et al. https://doi.org/10.1007/s00704-024-04901-1
4. Time-independent bias correction methods compared with gauge adjustment methods in improving radar-based precipitation estimates K. Yousefi et al. https://doi.org/10.1080/02626667.2023.2248108
5. Filling Data Gaps: Comparing Regression-based Models and Machine Learning Methods for Rainfall Time Series Reconstruction A. de Oliveira Pinheiro et al. https://doi.org/10.1007/s41748-026-01317-x
6. Impact of deep learning-driven precipitation corrected data using near real-time satellite-based observations and model forecast in an integrated hydrological model K. Patakchi Yousefi et al. https://doi.org/10.3389/frwa.2024.1439906
7. Moisture Source–Receptor Relationships for Post-Processing Medium-Range Precipitation Forecasts M. Dantanarayana & S. Kanae https://doi.org/10.1007/s44393-026-00025-z
8. Deep-learning-based sub-seasonal precipitation and streamflow ensemble forecasting over the source region of the Yangtze River N. Dong et al. https://doi.org/10.5194/hess-29-2023-2025
9. The applicability of statistical post-processing techniques for quantitative precipitation forecast in the Huaihe River Basin S. Xu et al. https://doi.org/10.1016/j.ejrh.2025.102988
10. Enhanced sequential soil moisture estimation using an ensemble deep learning filter without linear-Gaussian constraints T. Zhang et al. https://doi.org/10.1016/j.jhydrol.2025.134810
11. Multi-layer grid-scale soil moisture estimation using spatiotemporal deep learning methods with physical constraints T. Zhang et al. https://doi.org/10.1016/j.jhydrol.2025.133086
12. A national-scale hybrid model for enhanced streamflow estimation – consolidating a physically based hydrological model with long short-term memory (LSTM) networks J. Liu et al. https://doi.org/10.5194/hess-28-2871-2024
13. Improving ensemble forecast quality for heavy-to-extreme precipitation for the Meteorological Ensemble Forecast Processor via conditional bias-penalized regression S. Kim et al. https://doi.org/10.1016/j.jhydrol.2024.132363
14. Data assimilation enhanced WRF simulation of the 2018 Kerala flood: sensitivity to initial conditions, parameterization schemes and extreme rainfall prediction skill F. Rasla et al. https://doi.org/10.1007/s00704-026-06325-5