94 citations as recorded by crossref.

1. Machine Learning-Based Reconstructions of Historical Daily and Monthly Runoff for the Laurentian Great Lakes R. Gupta et al. https://doi.org/10.1038/s41597-026-07000-0
2. Bridging training–projection gaps in purely data-driven deep learning for runoff under climate change Y. Cen et al. https://doi.org/10.1016/j.jhydrol.2026.135508
3. Event-based training data thresholds for BiLSTM versus Xinanjiang models: Insights from the applications of 19 Chinese catchments Y. Li et al. https://doi.org/10.1016/j.ejrh.2026.103299
4. Regional scale simulations of daily suspended sediment concentration at gauged and ungauged rivers using deep learning A. Gupta & D. Feng https://doi.org/10.1016/j.jhydrol.2025.133111
5. How much historical data do we need? The role of data recency and training period length in LSTM-based rainfall-runoff modeling Q. Yu & B. Tolson https://doi.org/10.1016/j.jhydrol.2026.135046
6. Predicting abnormality-guided multimodal linguistic semantics Arabic image captioning N. Aljojo et al. https://doi.org/10.1016/j.mlwa.2025.100706
7. Machine learning in stream and river water temperature modeling: a review and metrics for evaluation C. Corona & T. Hogue https://doi.org/10.5194/hess-29-2521-2025
8. Characterizing the influence of remotely sensed wetland and lake water storage on discharge using LSTM models M. Vanderhoof et al. https://doi.org/10.1080/02626667.2025.2593333
9. An LSTM network for joint modeling of streamflow and hydropower generation for run-of-river plants T. Roksvåg et al. https://doi.org/10.1016/j.jhydrol.2025.134890
10. A hybrid statistical-dynamical forecast of seasonal streamflow for a catchment in the Upper Columbia River basin in Canada T. Swift-LaPointe et al. https://doi.org/10.3389/frwa.2025.1595898
11. Better continental-scale streamflow predictions for Australia: LSTM as a land surface model post-processor and standalone hydrological model A. Shokri et al. https://doi.org/10.5194/hess-30-757-2026
12. Challenges and opportunities of ML and explainable AI in large-sample hydrology L. Slater et al. https://doi.org/10.1098/rsta.2024.0287
13. How well do process-based and data-driven hydrological models learn from limited discharge data? M. Staudinger et al. https://doi.org/10.5194/hess-29-5005-2025
14. From gauged to ungauged: Large-scale deep learning rainfall-runoff modelling for reliable streamflow estimation in India's diverse basins S. Barbhuiya & V. Gupta https://doi.org/10.1016/j.envsoft.2025.106696
15. Unveiling the limits of deep learning models in hydrological extrapolation tasks S. Baste et al. https://doi.org/10.5194/hess-29-5871-2025
16. An explainable physics-aware deep learning framework with improved spatiotemporal dependence matrices and signal decomposition for multi-station uncertainty daily runoff simulation W. Wu et al. https://doi.org/10.1016/j.jhydrol.2026.135625
17. A python framework for differentiable hydrological modeling and research workflow automation W. Ouyang et al. https://doi.org/10.1016/j.envsoft.2026.106895
18. Towards a global spatial machine learning model for seasonal groundwater level predictions in Germany S. Kunz et al. https://doi.org/10.5194/hess-29-3405-2025
19. HydroEcoLSTM: A Python package with graphical user interface for hydro-ecological modeling with long short-term memory neural network T. Nguyen et al. https://doi.org/10.1016/j.ecoinf.2025.102994
20. Technical note: High Nash–Sutcliffe Efficiencies conceal poor simulations of interannual variance in seasonal regimes S. Ruzzante et al. https://doi.org/10.5194/hess-30-2337-2026
21. Application of Artificial Intelligence in Hydrological Modeling for Streamflow Prediction in Ungauged Watersheds: A Review J. Gacu et al. https://doi.org/10.3390/w17182722
22. Pooling local climate and donor gauges with deep learning for improved reconstructions of streamflow in ungauged and partially gauged basins S. Wi et al. https://doi.org/10.1016/j.jhydrol.2025.133764
23. Monte-Carlo-assisted endo-exo temporal transformer for high-confidence interval forecasting of daily runoff X. Hu et al. https://doi.org/10.1007/s00477-026-03206-1
24. A diversity-centric strategy for the selection of spatio-temporal training data for LSTM-based streamflow forecasting E. Snieder & U. Khan https://doi.org/10.5194/hess-29-785-2025
25. Exploring the ability of LSTM-based hydrological models to simulate streamflow time series for flood frequency analysis J. Martel et al. https://doi.org/10.5194/hess-29-4951-2025
26. An explainable AI approach for interpreting regionally optimized deep neural networks in hydrological prediction F. Hosseini et al. https://doi.org/10.1016/j.jhydrol.2025.133689
27. Learning to filter: snow data assimilation using a Long Short-Term Memory network G. Blandini et al. https://doi.org/10.5194/tc-19-4759-2025
28. Near-Real-Time Statistical Analysis and Visualization of Streamflow from a Deep-Learning Rainfall-Runoff Model T. Duong et al. https://doi.org/10.1007/s11269-026-04602-6
29. A differentiability-based processes and parameters learning hydrologic model for advancing runoff prediction and process understanding C. Zhang et al. https://doi.org/10.1016/j.jhydrol.2025.133594
30. Time fractional Saint Venant equations reveal the physical basis of hydrograph retardation through model comparison and field data H. Wei et al. https://doi.org/10.1038/s41598-025-23061-4
31. Knowledge-guided graph machine learning for spatially distributed prediction of daily discharge and nitrogen export dynamics J. Yang et al. https://doi.org/10.1016/j.watres.2026.125613
32. Spatially resolved rainfall streamflow modeling in central Europe M. Vischer et al. https://doi.org/10.5194/hess-29-5233-2025
33. A runoff prediction method for arid regions integrating physics-guided signal extraction and temporally adaptive feature selection Z. Li et al. https://doi.org/10.1016/j.ejrh.2025.103034
34. Analyzing the generalization capabilities of a hybrid hydrological model for extrapolation to extreme events E. Acuña Espinoza et al. https://doi.org/10.5194/hess-29-1277-2025
35. Analysis of Baseline and Novel Boosting Models for Flood-Prone Prediction and Explainability: Case from the Upper Drâa Basin (Morocco) L. Goumghar et al. https://doi.org/10.3390/earth6030069
36. Never Train a Deep Learning Model on a Single Well? Revisiting Training Strategies for Groundwater Level Prediction M. Ohmer & T. Liesch https://doi.org/10.5194/hess-30-2373-2026
37. A quantum-inspired attention integrated scalar long short-term memory model for accurate and stable groundwater contaminant source inversion L. Zhu & W. Lu https://doi.org/10.1007/s10661-025-14972-w
38. Distilling hydrological and land-surface model parameters from physio-geographical properties using text-generating AI M. Feigl et al. https://doi.org/10.1038/s44221-026-00583-3
39. IoT-enabled real-time health monitoring system for adolescent physical rehabilitation J. Yang et al. https://doi.org/10.1038/s41598-025-99838-4
40. Evaluating Rainfall-Runoff Generation Mechanisms of Deep Learning Models Using a Process-Based Rainfall-Runoff Model T. Duong et al. https://doi.org/10.1007/s11269-025-04231-5
41. A spatiotemporally differentiated hybrid hydrological modeling strategy with dynamically adaptive runoff generation modes X. Ma et al. https://doi.org/10.1016/j.jhydrol.2026.135480
42. Assessment of Potential Surface Runoff in Tulasi Watershed of Kolhapur Using NRSC-CN Method V. Pawar-Patil et al. https://doi.org/10.51847/Rd8ub5wo3G
43. Is smart sampling worth it? Impact of training data selection on the performance of LSTMs in streamflow prediction B. Heudorfer & R. Loritz https://doi.org/10.2166/nh.2026.119
44. A nonstationary stochastic simulator for clustered regional hydroclimatic extremes to characterize compound flood risk A. Nayak et al. https://doi.org/10.1016/j.hydroa.2024.100189
45. Regional vs local LSTM models for short-term streamflow forecasting under operational constraints J. Saavedra-Garrido et al. https://doi.org/10.1016/j.envsoft.2026.106897
46. Local vs regional neural air pollution forecasting models M. Sangiorgio & G. Guariso https://doi.org/10.1016/j.ifacsc.2025.100298
47. HydroGRAF: Hybrid discharge reconstruction and basin-aware streamflow forecasting in the Himalayas A. Gul et al. https://doi.org/10.1016/j.jhydrol.2026.135479
48. On the value of a history of hydrology and the establishment of a History of Hydrology Working Group K. Beven et al. https://doi.org/10.1080/02626667.2025.2452357
49. An interpretable machine learning approach for alkalinity reconstruction in the Mediterranean Sea T. Tonelli et al. https://doi.org/10.1016/j.acags.2026.100345
50. Deep learning for the probabilistic prediction of semi-continuous hydrological variables – An application to streamflow prediction across CONUS J. Quilty & M. Jahangir https://doi.org/10.1016/j.jhydrol.2026.134986
51. A novel hybrid fine-tuning method for supercharging deep learning model development for hydrological prediction M. Jahangir et al. https://doi.org/10.1016/j.envsoft.2026.106978
52. Empowering Regional Rainfall-Runoff Modeling Through Encoder–Decoder Based on Convolutional Neural Networks W. Jiang et al. https://doi.org/10.3390/w17030339
53. Fine-tuning long short-term memory models for seamless transition in hydrological modelling: From pre-training to post-application X. Chen et al. https://doi.org/10.1016/j.envsoft.2025.106350
54. Defense and Security Mechanisms in the Internet of Things: A Review S. Szymoniak et al. https://doi.org/10.3390/app15020499
55. Riverine heatwaves are an emergent climate change risk A. van Hamel et al. https://doi.org/10.1038/s44221-025-00541-5
56. On generalization, language, interpretability and the future of geo-scientific machine learning H. Gupta https://doi.org/10.1016/j.envsoft.2025.106834
57. Interpretable feature incorporation machine-learning framework for flood magnitude estimation E. Ford et al. https://doi.org/10.5194/hess-30-2135-2026
58. Overview of Modern Technologies for Acquiring and Analysing Acoustic Information Based on AI and IoT S. Szymoniak & Ł. Kuczyński https://doi.org/10.3390/app15126690
59. Toward routing river water in land surface models with recurrent neural networks M. Lima et al. https://doi.org/10.5194/hess-29-3145-2025
60. Deep learning for groundwater level simulation in unconfined aquifers across the contiguous United States: Analyzing simulations at multiple lead times and integrating groundwater signatures K. Boo et al. https://doi.org/10.1016/j.jhydrol.2026.134949
61. Catchment features-based interpretation of performance of the conceptual hydrological and deep learning models using large sample hydrologic data D. Sourya et al. https://doi.org/10.1016/j.jhydrol.2025.134270
62. Artificial intelligence with earth observations provides continuous streamflow data across varying wildfire recurrence and recovery scenarios S. Uddin et al. https://doi.org/10.1016/j.envsoft.2026.106989
63. Ensemble learning of catchment-wise optimized LSTMs enhances regional rainfall-runoff modelling − case Study: Basque Country, Spain F. Hosseini et al. https://doi.org/10.1016/j.jhydrol.2024.132269
64. Differentiable parameter learning of reservoir operation modules Z. Chen & T. Zhao https://doi.org/10.1016/j.jhydrol.2026.134915
65. A differentiable, physics-based hydrological model and its evaluation for data-limited basins W. Ouyang et al. https://doi.org/10.1016/j.jhydrol.2024.132471
66. HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin F. Kratzert et al. https://doi.org/10.5194/hess-28-4187-2024
67. Detection and attribution of eco-hydrological alteration based on deep learning-driven gap-filled runoff in a large-scale catchment Z. Dong et al. https://doi.org/10.1016/j.ejrh.2025.102228
68. Assessing the adequacy of traditional hydrological models for climate change impact studies: a case for long short-term memory (LSTM) neural networks J. Martel et al. https://doi.org/10.5194/hess-29-2811-2025
69. Strategies for incorporating static features into global deep learning models T. Liesch & M. Ohmer https://doi.org/10.5194/hess-30-1877-2026
70. Learning extreme vegetation response to climate drivers with recurrent neural networks F. Martinuzzi et al. https://doi.org/10.5194/npg-31-535-2024
71. Enhancing monthly runoff prediction in arid alpine basins of northwestern China by an EMD-PCA-LSTM hybrid model W. Hu et al. https://doi.org/10.1016/j.ejrh.2025.102748
72. Multi-site deep learning for groundwater level prediction across global datasets: toward scalable applications under data scarcity A. Nolte et al. https://doi.org/10.2166/hydro.2025.095
73. Bottom-up assessment of climate change vulnerability of a large and complex river basin using emulator models A. John et al. https://doi.org/10.1016/j.ejrh.2025.103095
74. How to deal w___ missing input data M. Gauch et al. https://doi.org/10.5194/hess-29-6221-2025
75. Calibrating a large-domain land/hydrology process model in the age of AI: the SUMMA CAMELS emulator experiments M. Farahani et al. https://doi.org/10.5194/hess-29-4515-2025
76. A Comprehensive Calibration Framework for the Northwest River Forecast Center G. Walters et al. https://doi.org/10.1111/1752-1688.70112
77. Transfer learning using the global Caravan dataset for developing a local river streamflow prediction model A. Alzhanov et al. https://doi.org/10.1016/j.envsoft.2025.106691
78. CH-RUN: a deep-learning-based spatially contiguous runoff reconstruction for Switzerland B. Kraft et al. https://doi.org/10.5194/hess-29-1061-2025
79. Machine Learning-Based Process Optimization in Biopolymer Manufacturing: A Review I. Malashin et al. https://doi.org/10.3390/polym16233368
80. DeepDiscover: towards autonomous discovery of bucket-type conceptual models – a proof of concept applied to hydrology A. Adombi https://doi.org/10.1016/j.jhydrol.2026.135249
81. Assessing groundwater level modelling using a 1-D convolutional neural network (CNN): linking model performances to geospatial and time series features M. Gomez et al. https://doi.org/10.5194/hess-28-4407-2024
82. Future projections of China runoff changes based on CMIP6 and deep learning X. Wei et al. https://doi.org/10.1016/j.ejrh.2025.102998
83. When physics gets in the way: an entropy-based evaluation of conceptual constraints in hybrid hydrological models M. Álvarez Chaves et al. https://doi.org/10.5194/hess-30-629-2026
84. Are LSTM and conceptual rainfall-runoff models able to cope with limited training datasets under diverse hydrometeorological conditions? F. Boodoo et al. https://doi.org/10.1007/s40808-025-02316-z
85. Improving annual streamflow estimates using Budyko parameterization driven by catchment attributes H. Tamiru et al. https://doi.org/10.1016/j.hydroa.2026.100216
86. Detecting differentiated services code point values and packet length mismatch in internet protocol packet headers M. A. Aldhahery https://doi.org/10.7717/peerj-cs.3471
87. Exploring a process-aware spatiotemporal graph-based surrogate for integrated urban drainage simulation B. Yin et al. https://doi.org/10.1016/j.jhydrol.2026.135350
88. Riverine heat waves on the rise, outpacing air heat waves K. Sadayappan & L. Li https://doi.org/10.1073/pnas.2503160122
89. Abnormal behavior modeling and intelligent error prevention algorithm in AI load forecasting S. Zhang & Q. Han https://doi.org/10.1177/18724981251390977
90. Hydrological insights from a comparative evaluation of LSTM and MC-LSTM networks in the Ebro River basin (Spain) I. González-Planet & C. Juez https://doi.org/10.1016/j.ejrh.2025.102719
91. Streamflow Forecasting: A Comparative Analysis of ARIMAX, Rolling Forecasting LSTM Neural Network and Physically Based Models in a Pristine Catchment D. Perazzolo et al. https://doi.org/10.3390/w17152341
92. Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell E. Acuña Espinoza et al. https://doi.org/10.5194/hess-29-1749-2025
93. An Enhanced Hybrid LSTM–Linear Regression Framework for 90-Day Rainfall Forecasting in Rainfed Agricultural Regions A. Kunlerd et al. https://doi.org/10.48084/etasr.15622
94. A novel hybrid deep learning model with dynamic parameterization for accurate flood simulation B. Sun et al. https://doi.org/10.1016/j.jhydrol.2026.135182