70 citations as recorded by crossref.

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21. 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
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24. 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
25. Predictive Performance of Ensemble Learning Boosting Techniques in Daily Streamflow Simulation D. Chandran & N. Chithra 10.1007/s11269-024-04029-x
26. Application of Deep Learning in Drainage Systems Monitoring Data Repair—A Case Study Using Con-GRU Model L. He et al. 10.3390/w15081635
27. Generative deep learning for probabilistic streamflow forecasting: Conditional variational auto-encoder M. Jahangir & J. Quilty 10.1016/j.jhydrol.2023.130498
28. 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
29. 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
30. Uncertainties in different leak localization methods for water distribution networks: a review P. Mohan Doss et al. 10.1080/1573062X.2023.2229301
31. 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
32. Runoff Simulation in Data-Scarce Alpine Regions: Comparative Analysis Based on LSTM and Physically Based Models J. Yue et al. 10.3390/w16152161
33. Flood forecasting with machine learning models in an operational framework S. Nevo et al. 10.5194/hess-26-4013-2022
34. Response to hydrometeorological changes and evaluation of habitat quality in the Dongting Lake basin, China H. Wang et al. 10.2166/wcc.2024.179
35. 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
36. 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
37. 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
38. A short history of philosophies of hydrological model evaluation and hypothesis testing K. Beven 10.1002/wat2.1761
39. 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
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41. 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
42. A Physics‐Aware Machine Learning‐Based Framework for Minimizing Prediction Uncertainty of Hydrological Models A. Roy et al. 10.1029/2023WR034630
43. 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
44. EEG-Based Seizure Prediction via Model Uncertainty Learning C. Li et al. 10.1109/TNSRE.2022.3217929
45. Bayesian extreme learning machines for hydrological prediction uncertainty J. Quilty et al. 10.1016/j.jhydrol.2023.130138
46. Advancement of a Blended Hydrologic Model for Robust Model Performance R. Chlumsky et al. 10.1061/JHYEFF.HEENG-6246
47. 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
48. 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
49. Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins S. Tang et al. 10.1029/2022WR034352
50. Learning landscape features from streamflow with autoencoders A. Bassi et al. 10.5194/hess-28-4971-2024
51. Monthly Runoff Prediction Via Mode Decomposition-Recombination Technique X. Yang et al. 10.1007/s11269-023-03668-w
52. 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
53. Deep transfer learning based on transformer for flood forecasting in data-sparse basins Y. Xu et al. 10.1016/j.jhydrol.2023.129956
54. 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
55. Deep learning in hydrology and water resources disciplines: concepts, methods, applications, and research directions K. Tripathy & A. Mishra 10.1016/j.jhydrol.2023.130458
56. 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
57. 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
58. How to enhance hydrological predictions in hydrologically distinct watersheds of the Indian subcontinent? N. Mangukiya et al. 10.1002/hyp.14936
59. 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
60. Uncertainty Analysis in Multi‐Sector Systems: Considerations for Risk Analysis, Projection, and Planning for Complex Systems V. Srikrishnan et al. 10.1029/2021EF002644
61. A data-centric perspective on the information needed for hydrological uncertainty predictions A. Auer et al. 10.5194/hess-28-4099-2024
62. 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
63. NeuralHydrology — A Python library for Deep Learning research in hydrology F. Kratzert et al. 10.21105/joss.04050
64. Niederschlags-Abfluss-Modellierung mit Long Short-Term Memory (LSTM) F. Kratzert et al. 10.1007/s00506-021-00767-z
65. Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network M. Gauch et al. 10.5194/hess-25-2045-2021
66. A Multiscale Deep Learning Model for Soil Moisture Integrating Satellite and In Situ Data J. Liu et al. 10.1029/2021GL096847
67. 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
68. 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
69. A New Rainfall-Runoff Model Using Improved LSTM with Attentive Long and Short Lag-Time X. Chen et al. 10.3390/w14050697
70. 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