53 citations as recorded by crossref.

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7. Performance of LSTM over SWAT in Rainfall-Runoff Modeling in a Small, Forested Watershed: A Case Study of Cork Brook, RI S. Shrestha & S. Pradhanang 10.3390/w15234194
8. 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
9. WATER LEVEL PREDICTIONS THAT VISUALIZE UNCERTAINTY USING LSTM COUPLED WITH ALTERNATIVE METHODS IN BAYESIAN INFERENCE AT A RESERVOIR NEAR A DRAINAGE PUMPING STATION N. KIMURA et al. 10.2208/jscejj.23-22011
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12. On the need for physical constraints in deep learning rainfall–runoff projections under climate change: a sensitivity analysis to warming and shifts in potential evapotranspiration S. Wi & S. Steinschneider 10.5194/hess-28-479-2024
13. A Physics‐Aware Machine Learning‐Based Framework for Minimizing Prediction Uncertainty of Hydrological Models A. Roy et al. 10.1029/2023WR034630
14. ML-DPIE: comparative evaluation of machine learning methods for drought parameter index estimation: a case study of Türkiye Ö. Çoban et al. 10.1007/s11069-023-06233-1
15. Evaluating the parameter sensitivity and impact of hydrologic modeling decisions on flood simulations A. Alexander et al. 10.1016/j.advwatres.2023.104560
16. EEG-Based Seizure Prediction via Model Uncertainty Learning C. Li et al. 10.1109/TNSRE.2022.3217929
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18. 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
19. Uncertainty quantification of machine learning models to improve streamflow prediction under changing climate and environmental conditions S. Liu et al. 10.3389/frwa.2023.1150126
20. Streamflow forecast model using ANN D. Sonowal et al. 10.1080/23249676.2024.2310098
21. Uncertainty in Environmental Micropollutant Modeling H. Ahkola et al. 10.1007/s00267-024-01989-z
22. Hydropower flexibility valuation tool for flow requirement evaluation M. Roni et al. 10.1016/j.egyr.2022.11.172
23. Self-training approach to improve the predictability of data-driven rainfall-runoff model in hydrological data-sparse regions S. Yoon & K. Ahn 10.1016/j.jhydrol.2024.130862
24. Global prediction of extreme floods in ungauged watersheds G. Nearing et al. 10.1038/s41586-024-07145-1
25. Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins S. Tang et al. 10.1029/2022WR034352
26. Monthly Runoff Prediction Via Mode Decomposition-Recombination Technique X. Yang et al. 10.1007/s11269-023-03668-w
27. The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL) J. Mai et al. 10.5194/hess-26-3537-2022
28. Transfer learning framework for streamflow prediction in large-scale transboundary catchments: Sensitivity analysis and applicability in data-scarce basins K. Ma et al. 10.1007/s11442-024-2235-x
29. Deep transfer learning based on transformer for flood forecasting in data-sparse basins Y. Xu et al. 10.1016/j.jhydrol.2023.129956
30. Multi-Step Ahead Probabilistic Forecasting of Daily Streamflow Using Bayesian Deep Learning: A Multiple Case Study F. Ghobadi & D. Kang 10.3390/w14223672
31. Global patterns in water flux partitioning: Irrigated and rainfed agriculture drives asymmetrical flux to vegetation over runoff D. Althoff & G. Destouni 10.1016/j.oneear.2023.08.002
32. Reconstruction of missing streamflow series in human-regulated catchments using a data integration LSTM model A. Tursun et al. 10.1016/j.ejrh.2024.101744
33. A Case Study of Tidal Analysis Using Theory-Based Artificial Intelligence Techniques for Disaster Management in Taehwa River, South Korea K. Kareem et al. 10.3390/w14142172
34. Deep learning in hydrology and water resources disciplines: concepts, methods, applications, and research directions K. Tripathy & A. Mishra 10.1016/j.jhydrol.2023.130458
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36. Deep learning rainfall–runoff predictions of extreme events J. Frame et al. 10.5194/hess-26-3377-2022
37. Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States K. Hunt et al. 10.5194/hess-26-5449-2022
38. LSTM-Based Model for Predicting Inland River Runoff in Arid Region: A Case Study on Yarkant River, Northwest China J. Li et al. 10.3390/w14111745
39. Application of Deep Learning in Drainage Systems Monitoring Data Repair—A Case Study Using Con-GRU Model L. He et al. 10.3390/w15081635
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41. 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. 10.5194/hess-28-2871-2024
42. Generative deep learning for probabilistic streamflow forecasting: Conditional variational auto-encoder M. Jahangir & J. Quilty 10.1016/j.jhydrol.2023.130498
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44. Data reformation – A novel data processing technique enhancing machine learning applicability for predicting streamflow extremes V. Tran et al. 10.1016/j.advwatres.2023.104569
45. Is the deep-learning technique a completely alternative for the hydrological model?: A case study on Hyeongsan River Basin, Korea J. Kwak et al. 10.1007/s00477-021-02094-x
46. NeuralHydrology — A Python library for Deep Learning research in hydrology F. Kratzert et al. 10.21105/joss.04050
47. Niederschlags-Abfluss-Modellierung mit Long Short-Term Memory (LSTM) F. Kratzert et al. 10.1007/s00506-021-00767-z
48. Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network M. Gauch et al. 10.5194/hess-25-2045-2021
49. A Multiscale Deep Learning Model for Soil Moisture Integrating Satellite and In Situ Data J. Liu et al. 10.1029/2021GL096847
50. Near‐term forecasts of stream temperature using deep learning and data assimilation in support of management decisions J. Zwart et al. 10.1111/1752-1688.13093
51. Exploring the Potential of Long Short‐Term Memory Networks for Improving Understanding of Continental‐ and Regional‐Scale Snowpack Dynamics Y. Wang et al. 10.1029/2021WR031033
52. A New Rainfall-Runoff Model Using Improved LSTM with Attentive Long and Short Lag-Time X. Chen et al. 10.3390/w14050697
53. A Novel Approach to Uncertainty Quantification in Groundwater Table Modeling by Automated Predictive Deep Learning A. Abbaszadeh Shahri et al. 10.1007/s11053-022-10051-w