8 citations as recorded by crossref.

1. CEREF: A hybrid data-driven model for forecasting annual streamflow from a socio-hydrological system H. Zhang et al. https://doi.org/10.1016/j.jhydrol.2016.06.029
2. A computer vision-based approach to fusing spatiotemporal data for hydrological modeling S. Jiang et al. https://doi.org/10.1016/j.jhydrol.2018.09.064
3. An Ensemble Empirical Mode Decomposition, Self-Organizing Map, and Linear Genetic Programming Approach for Forecasting River Streamflow J. Barge & H. Sharif https://doi.org/10.3390/w8060247
4. Improvement for the multi-scale periodic characteristics revealing of precipitation signals and its impact assessment on soil hydrological process by combining HHT and CWT approaches S. Yu et al. https://doi.org/10.5194/nhess-15-393-2015
5. Improving Ann‐Based Short‐Term and Long‐Term Seasonal River Flow Forecasting with Signal Processing Techniques H. Badrzadeh et al. https://doi.org/10.1002/rra.2865
6. Estimating water levels and discharges in tidal rivers and estuaries: Review of machine learning approaches A. Mihel et al. https://doi.org/10.1016/j.envsoft.2024.106033
7. Heuristic Methods for Reservoir Monthly Inflow Forecasting: A Case Study of Xinfengjiang Reservoir in Pearl River, China C. Cheng et al. https://doi.org/10.3390/w7084477
8. Incorporating Empirical Orthogonal Function Analysis into Machine Learning Models for Streamflow Prediction Y. Wu et al. https://doi.org/10.3390/su14116612