79 citations as recorded by crossref.

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25. Technical note: How many models do we need to simulate hydrologic processes across large geographical domains? W. Knoben et al. 10.5194/hess-29-2361-2025
26. 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
27. Deep learning rainfall–runoff predictions of extreme events J. Frame et al. 10.5194/hess-26-3377-2022
28. Ensemble post-processing of sub-seasonal to seasonal precipitation forecasts based on a novel probabilistic double machine learning method S. Zhan et al. 10.1016/j.jhydrol.2025.133484
29. 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
30. Predictive Performance of Ensemble Learning Boosting Techniques in Daily Streamflow Simulation D. Chandran & N. Chithra 10.1007/s11269-024-04029-x
31. Evidential uncertainty quantification with multiple deep learning architectures for spatiotemporal drought forecasting A. Ferchichi et al. 10.1007/s00521-025-11026-7
32. Application of Deep Learning in Drainage Systems Monitoring Data Repair—A Case Study Using Con-GRU Model L. He et al. 10.3390/w15081635
33. Generative deep learning for probabilistic streamflow forecasting: Conditional variational auto-encoder M. Jahangir & J. Quilty 10.1016/j.jhydrol.2023.130498
34. Harnessing Novel Data‐Driven Techniques for Precise Rainfall–Runoff Modeling S. Sammen et al. 10.1111/jfr3.70013
35. Streamflow forecasting with deep learning models: A side-by-side comparison in Northwest Spain J. Farfán-Durán & L. Cea 10.1007/s12145-024-01454-9
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38. Enhancing streamflow simulation in large and human-regulated basins: Long short-term memory with multiscale attributes A. Tursun et al. 10.1016/j.jhydrol.2024.130771
39. Runoff Simulation in Data-Scarce Alpine Regions: Comparative Analysis Based on LSTM and Physically Based Models J. Yue et al. 10.3390/w16152161
40. Flood forecasting with machine learning models in an operational framework S. Nevo et al. 10.5194/hess-26-4013-2022
41. Training deep learning models with a multi-station approach and static aquifer attributes for groundwater level simulation: what is the best way to leverage regionalised information? S. Chidepudi et al. 10.5194/hess-29-841-2025
42. Response to hydrometeorological changes and evaluation of habitat quality in the Dongting Lake basin, China H. Wang et al. 10.2166/wcc.2024.179
43. Use of one-dimensional CNN for input data size reduction in LSTM for improved computational efficiency and accuracy in hourly rainfall-runoff modeling K. Ishida et al. 10.1016/j.jenvman.2024.120931
44. 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
45. 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
46. A short history of philosophies of hydrological model evaluation and hypothesis testing K. Beven 10.1002/wat2.1761
47. Hyperparameter optimization of regional hydrological LSTMs by random search: A case study from Basque Country, Spain F. Hosseini et al. 10.1016/j.jhydrol.2024.132003
48. Technical note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks G. Nearing et al. 10.5194/hess-26-5493-2022
49. 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
50. A Physics‐Aware Machine Learning‐Based Framework for Minimizing Prediction Uncertainty of Hydrological Models A. Roy et al. 10.1029/2023WR034630
51. 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
52. EEG-Based Seizure Prediction via Model Uncertainty Learning C. Li et al. 10.1109/TNSRE.2022.3217929
53. Bayesian extreme learning machines for hydrological prediction uncertainty J. Quilty et al. 10.1016/j.jhydrol.2023.130138
54. Advancement of a Blended Hydrologic Model for Robust Model Performance R. Chlumsky et al. 10.1061/JHYEFF.HEENG-6246
55. Optimizing parameter estimation in hydrological models with convolutional neural network guided dynamically dimensioned search approach A. Alexander & D. Kumar 10.1016/j.advwatres.2024.104842
56. 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
57. Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins S. Tang et al. 10.1029/2022WR034352
58. Learning landscape features from streamflow with autoencoders A. Bassi et al. 10.5194/hess-28-4971-2024
59. Monthly Runoff Prediction Via Mode Decomposition-Recombination Technique X. Yang et al. 10.1007/s11269-023-03668-w
60. 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
61. Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell E. Acuña Espinoza et al. 10.5194/hess-29-1749-2025
62. Deep transfer learning based on transformer for flood forecasting in data-sparse basins Y. Xu et al. 10.1016/j.jhydrol.2023.129956
63. 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
64. Deep learning in hydrology and water resources disciplines: concepts, methods, applications, and research directions K. Tripathy & A. Mishra 10.1016/j.jhydrol.2023.130458
65. A deep learning modeling framework with uncertainty quantification for inflow-outflow predictions for cascade reservoirs V. Ngoc Tran et al. 10.1016/j.jhydrol.2024.130608
66. 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
67. How to enhance hydrological predictions in hydrologically distinct watersheds of the Indian subcontinent? N. Mangukiya et al. 10.1002/hyp.14936
68. 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
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71. 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
72. NeuralHydrology — A Python library for Deep Learning research in hydrology F. Kratzert et al. 10.21105/joss.04050
73. Niederschlags-Abfluss-Modellierung mit Long Short-Term Memory (LSTM) F. Kratzert et al. 10.1007/s00506-021-00767-z
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75. A Multiscale Deep Learning Model for Soil Moisture Integrating Satellite and In Situ Data J. Liu et al. 10.1029/2021GL096847
76. 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
77. 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
78. A New Rainfall-Runoff Model Using Improved LSTM with Attentive Long and Short Lag-Time X. Chen et al. 10.3390/w14050697
79. 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