51 citations as recorded by crossref.

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4. A state-of-the-art review of long short-term memory models with applications in hydrology and water resources Z. Feng et al. 10.1016/j.asoc.2024.112352
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7. Forecasting short- and medium-term streamflow using stacked ensemble models and different meta-learners F. Granata & F. Di Nunno 10.1007/s00477-024-02760-w
8. Hybrid physically based and machine learning model to enhance high streamflow prediction S. López-Chacón et al. 10.1080/02626667.2024.2426720
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10. Ensemble Forecasts of Extreme Flood Events with Weather Forecasts, Land Surface Modeling and Deep Learning Y. Liu et al. 10.3390/w16070990
11. 长江流域洞庭湖区出入湖磷通量模拟及水质预测：机器学习与传统水文模型耦合方法 J. Liu et al. 10.3799/dqkx.2024.061
12. MTV19ANet: A Multi-tier Visual Geometry Group 19 with Attention Network-Based Streamflow Prediction System S. A et al. 10.1007/s11269-025-04113-w
13. Impact Assessment of Coupling Mode of Hydrological Model and Machine Learning Model on Runoff Simulation: A Case of Washington J. Zhang et al. 10.3390/atmos15121461
14. Assessing the simulation of streamflow with the LSTM model across the continental United States using the MOPEX dataset A. Tounsi et al. 10.1007/s00521-023-08922-1
15. A spatiotemporal graph convolution-based model for daily runoff prediction in a river network with non-Euclidean topological structure L. Deng et al. 10.1007/s00477-022-02352-6
16. Enhanced streamflow simulations using nudging based optimization coupled with data-driven and hydrological models S. Thalli Mani et al. 10.1016/j.ejrh.2022.101190
17. Scale Effects of the Monthly Streamflow Prediction Using a State-of-the-art Deep Learning Model W. Xu et al. 10.1007/s11269-022-03216-y
18. Combining Synthetic and Observed Data to Enhance Machine Learning Model Performance for Streamflow Prediction S. López-Chacón et al. 10.3390/w15112020
19. Data‐driven artificial intelligence‐based streamflow forecasting, a review of methods, applications, and tools H. Jahanbani et al. 10.1111/1752-1688.13229
20. A new non-stationary standardised streamflow index using the climate indices and the optimal anthropogenic indices as covariates in the Wei River Basin, China M. Ren et al. 10.1016/j.ejrh.2023.101649
21. Improving the probabilistic drought prediction with soil moisture information under the ensemble streamflow prediction framework G. Kim et al. 10.1007/s00477-024-02710-6
22. Machine learning for postprocessing ensemble streamflow forecasts S. Sharma et al. 10.2166/hydro.2022.114
23. A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting G. Papacharalampous & H. Tyralis 10.3389/frwa.2022.961954
24. A Two‐Stage Framework for Bias and Reliability Tests of Ensemble Hydroclimatic Forecasts T. Zhao et al. 10.1029/2022WR032568
25. Analysis of statistical post-processing methods for multi-model ensemble runoff forecasts in flood seasons B. Qu et al. 10.1088/1755-1315/1087/1/012052
26. Enhancing physically-based hydrological modeling with an ensemble of machine-learning reservoir operation modules under heavy human regulation using easily accessible data T. Tu et al. 10.1016/j.jenvman.2024.121044
27. Near-term forecasting of water reservoir storage capacities using long short-term memory E. Rohli et al. 10.1017/eds.2023.25
28. Adapting subseasonal-to-seasonal (S2S) precipitation forecast at watersheds for hydrologic ensemble streamflow forecasting with a machine learning-based post-processing approach L. Zhang et al. 10.1016/j.jhydrol.2024.130643
29. Improving cascade reservoir inflow forecasting and extracting insights by decomposing the physical process using a hybrid model J. Li et al. 10.1016/j.jhydrol.2024.130623
30. Advancing Medium-Range Streamflow Forecasting for Large Hydropower Reservoirs in Brazil by Means of Continental-Scale Hydrological Modeling A. Kolling Neto et al. 10.3390/w15091693
31. Combining Satellite Imagery and a Deep Learning Algorithm to Retrieve the Water Levels of Small Reservoirs J. Wu et al. 10.3390/rs15245740
32. Improving the predictive skills of hydrological models using a combinatorial optimization algorithm and artificial neural networks J. Farfán & L. Cea 10.1007/s40808-022-01540-1
33. Informer-Based Safety Risk Prediction of Heavy Metals in Rice in China P. Lu et al. 10.3390/foods12030542
34. Spatiotemporal characteristics and forecasting of short-term meteorological drought in China Q. Zhang et al. 10.1016/j.jhydrol.2023.129924
35. Impact assessment of cascade freshwater reservoir using the ecological security assessment (ESA) model across a four-year timescale J. Yin et al. 10.1016/j.ecolind.2023.110907
36. Application of Machine Learning in Water Resources Management: A Systematic Literature Review F. Ghobadi & D. Kang 10.3390/w15040620
37. Enhanced Runoff Modeling by Incorporating Information from the GR4J Hydrological Model and Multiple Remotely Sensed Precipitation Datasets C. Mo et al. 10.3390/w16040530
38. Improving medium-range streamflow forecasts over South Korea with a dual-encoder transformer model D. Lee & K. Ahn 10.1016/j.jenvman.2024.122114
39. Cascade Reservoirs: An Exploration of Spatial Runoff Storage Sites for Water Harvesting and Mitigation of Climate Change Impacts, Using an Integrated Approach of GIS and Hydrological Modeling A. Soomro et al. 10.3390/su142013538
40. Modeling sediment flow analysis for hydro-electric projects using deep neural networks S. Tomar et al. 10.1007/s12145-024-01671-2
41. A long lead time forecast model applying an ensemble approach for managing the great Karun multi-reservoir system E. Mostaghimzadeh et al. 10.1007/s13201-023-01924-3
42. Improving Daily and Monthly River Discharge Forecasts using Geostatistical Ensemble Modeling F. Rezaei et al. 10.1007/s11269-022-03292-0
43. Deep Learning Prediction of Streamflow in Portugal R. Francisco & J. Matos 10.3390/hydrology11120217
44. A review of hybrid deep learning applications for streamflow forecasting K. Ng et al. 10.1016/j.jhydrol.2023.130141
45. Daily runoff forecasting using novel optimized machine learning methods P. Parisouj et al. 10.1016/j.rineng.2024.103319
46. Application of a New Hybrid Deep Learning Model That Considers Temporal and Feature Dependencies in Rainfall–Runoff Simulation F. Zhou et al. 10.3390/rs15051395
47. Applying Machine Learning Methods to Improve Rainfall–Runoff Modeling in Subtropical River Basins H. Yu & Q. Yang 10.3390/w16152199
48. Daily Streamflow Forecasts Based on Cascade Long Short-Term Memory (LSTM) Model over the Yangtze River Basin J. Li & X. Yuan 10.3390/w15061019
49. Climate warming outweighs vegetation greening in intensifying flash droughts over China M. Zhang et al. 10.1088/1748-9326/ac69fb
50. Comparison of different ensemble precipitation forecast system evaluation, integration and hydrological applications Y. Tang et al. 10.1007/s11600-022-00877-6
51. Multi-objective operation of cascade reservoirs based on short-term ensemble streamflow prediction S. He et al. 10.1016/j.jhydrol.2022.127936