52 citations as recorded by crossref.

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4. Uncertainty Forecasting Model for Mountain Flood Based on Bayesian Deep Learning S. Wang & O. Xu 10.1109/ACCESS.2024.3384066
5. The effects of spatial and temporal resolution of gridded meteorological forcing on watershed hydrological responses P. Shuai et al. 10.5194/hess-26-2245-2022
6. Runoff Estimation in the Upper Reaches of the Heihe River Using an LSTM Model with Remote Sensing Data H. Xue et al. 10.3390/rs14102488
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10. 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
11. 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
12. A data-centric perspective on the information needed for hydrological uncertainty predictions A. Auer et al. 10.5194/hess-28-4099-2024
13. Improving the interpretability and predictive power of hydrological models: Applications for daily streamflow in managed and unmanaged catchments P. Bhasme & U. Bhatia 10.1016/j.jhydrol.2023.130421
14. Assessing Objective Functions in Streamflow Prediction Model Training Based on the Naïve Method Y. Lin et al. 10.3390/w16050777
15. Temporal Fusion Transformers for streamflow Prediction: Value of combining attention with recurrence S. Rasiya Koya & T. Roy 10.1016/j.jhydrol.2024.131301
16. 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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19. 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
20. Assessing the Physical Realism of Deep Learning Hydrologic Model Projections Under Climate Change S. Wi & S. Steinschneider 10.1029/2022WR032123
21. Adjusting rainfall inputs to hydrological models in a data-scarce area of Southern Africa D. Hughes 10.1080/02626667.2024.2360971
22. Leveraging historic streamflow and weather data with deep learning for enhanced streamflow predictions C. Schutte et al. 10.2166/hydro.2024.268
23. Uncertainty estimation with deep learning for rainfall–runoff modeling D. Klotz et al. 10.5194/hess-26-1673-2022
24. A graph neural network (GNN) approach to basin-scale river network learning: the role of physics-based connectivity and data fusion A. Sun et al. 10.5194/hess-26-5163-2022
25. Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins S. Tang et al. 10.1029/2022WR034352
26. HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin F. Kratzert et al. 10.5194/hess-28-4187-2024
27. Enhancing process-based hydrological models with embedded neural networks: A hybrid approach B. Li et al. 10.1016/j.jhydrol.2023.130107
28. A novel feature attention mechanism for improving the accuracy and robustness of runoff forecasting H. Wang et al. 10.1016/j.jhydrol.2023.129200
29. The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment D. Feng et al. 10.5194/hess-27-2357-2023
30. 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
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37. Two novel error-updating model frameworks for short-to-medium range streamflow forecasting using bias-corrected rainfall inputs: Development and comparative assessment A. Khatun et al. 10.1016/j.jhydrol.2023.129199
38. On strictly enforced mass conservation constraints for modelling the Rainfall‐Runoff process J. Frame et al. 10.1002/hyp.14847
39. Pre-processing satellite rainfall products improves hydrological simulations with machine learning T. Boulmaiz et al. 10.1080/02626667.2024.2378108
40. Improving flood forecast accuracy based on explainable convolutional neural network by Grad-CAM method X. Xiang et al. 10.1016/j.jhydrol.2024.131867
41. Hybrid Deep Learning Time-Lagged Precipitation-Runoff Model T. Calvette et al. 10.1061/JHYEFF.HEENG-6222
42. Mapping of snow water equivalent by a deep-learning model assimilating snow observations G. Cui et al. 10.1016/j.jhydrol.2022.128835
43. Learning landscape features from streamflow with autoencoders A. Bassi et al. 10.5194/hess-28-4971-2024
44. Advancing subseasonal reservoir inflow forecasts using an explainable machine learning method M. Fan et al. 10.1016/j.ejrh.2023.101584
45. Predicting streamflow with LSTM networks using global datasets K. Wilbrand et al. 10.3389/frwa.2023.1166124
46. 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
47. Transferring Hydrologic Data Across Continents – Leveraging Data‐Rich Regions to Improve Hydrologic Prediction in Data‐Sparse Regions K. Ma et al. 10.1029/2020WR028600
48. Adaptability of machine learning methods and hydrological models to discharge simulations in data-sparse glaciated watersheds H. Ji et al. 10.1007/s40333-021-0066-5
49. Differentiable, Learnable, Regionalized Process‐Based Models With Multiphysical Outputs can Approach State‐Of‐The‐Art Hydrologic Prediction Accuracy D. Feng et al. 10.1029/2022WR032404
50. Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network M. Gauch et al. 10.5194/hess-25-2045-2021
51. Niederschlags-Abfluss-Modellierung mit Long Short-Term Memory (LSTM) F. Kratzert et al. 10.1007/s00506-021-00767-z
52. Addressing hydrological modeling in watersheds under land cover change with deep learning D. Althoff et al. 10.1016/j.advwatres.2021.103965