100 citations as recorded by crossref.

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2. Assimilation of remotely sensed evapotranspiration products for streamflow simulation based on the CAMELS data sets C. Deng et al. 10.1016/j.jhydrol.2023.130574
3. Attribution of Runoff Variation in Reservoir Construction Area: Based on a Merged Deep Learning Model and the Budyko Framework L. Zhang et al. 10.3390/atmos15020164
4. Evaluation of GPM IMERG satellite precipitation for rainfall–runoff modelling in Great Britain J. Gautam et al. 10.1080/02626667.2024.2394172
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6. 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
7. Value of process understanding in the era of machine learning: A case for recession flow prediction P. Istalkar et al. 10.1016/j.jhydrol.2023.130350
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11. A short history of philosophies of hydrological model evaluation and hypothesis testing K. Beven 10.1002/wat2.1761
12. Development and application of machine learning models in US consumer price index forecasting: Analysis of a hybrid approach Y. Gur 10.3934/DSFE
13. Multi-model ensemble benchmark data for hydrological modeling in Japanese river basins Y. Sawada et al. 10.3178/hrl.16.73
14. The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL) J. Mai et al. 10.5194/hess-26-3537-2022
15. Physics-informed machine learning for understanding rock moisture dynamics in a sandstone cave K. Ouyang et al. 10.5194/hess-27-2579-2023
16. Linking explainable artificial intelligence and soil moisture dynamics in a machine learning streamflow model A. Ley et al. 10.2166/nh.2024.003
17. Deep learning-based algorithms for long-term prediction of chlorophyll-a in catchment streams A. Abbas et al. 10.1016/j.jhydrol.2023.130240
18. 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
19. Simulated annealing coupled with a Naïve Bayes model and base flow separation for streamflow simulation in a snow dominated basin H. Tongal & M. Booij 10.1007/s00477-022-02276-1
20. Predicting streamflow with LSTM networks using global datasets K. Wilbrand et al. 10.3389/frwa.2023.1166124
21. Development and application of machine learning models in US consumer price index forecasting: Analysis of a hybrid approach Y. Gur 10.3934/DSFE.2024020
22. Generalization of an Encoder-Decoder LSTM model for flood prediction in ungauged catchments Y. Zhang et al. 10.1016/j.jhydrol.2022.128577
23. Has unsustainable groundwater use induced low flow regimes in the Urucuia Aquifer System? An urgent call for integrated water management A. Rodrigues et al. 10.1016/j.jenvman.2024.122979
24. Broadleaf afforestation impacts on terrestrial hydrology insignificant compared to climate change in Great Britain M. Buechel et al. 10.5194/hess-28-2081-2024
25. Runoff predictions in new-gauged basins using two transformer-based models H. Yin et al. 10.1016/j.jhydrol.2023.129684
26. To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization E. Acuña Espinoza et al. 10.5194/hess-28-2705-2024
27. Low-flow estimation beyond the mean – expectile loss and extreme gradient boosting for spatiotemporal low-flow prediction in Austria J. Laimighofer et al. 10.5194/hess-26-4553-2022
28. Green roofs and their effect on architectural design and urban ecology using deep learning approaches C. Wang et al. 10.1007/s00500-024-09637-8
29. Deep dive into hydrologic simulations at global scale: harnessing the power of deep learning and physics-informed differentiable models (δHBV-globe1.0-hydroDL) D. Feng et al. 10.5194/gmd-17-7181-2024
30. Application of LSTM considering time steps in runoff prediction of Ganjiang River Basin H. Leyi et al. 10.18307/2024.0454
31. Integrating Euclidean and non-Euclidean spatial information for deep learning-based spatiotemporal hydrological simulation L. Deng et al. 10.1016/j.jhydrol.2024.131438
32. A quality-control framework for sub-daily flow and level data for hydrological modelling in Great Britain F. Fileni et al. 10.2166/nh.2023.045
33. RR-Former: Rainfall-runoff modeling based on Transformer H. Yin et al. 10.1016/j.jhydrol.2022.127781
34. Large-sample hydrology – a few camels or a whole caravan? F. Clerc-Schwarzenbach et al. 10.5194/hess-28-4219-2024
35. Potenzial von Machine Learning bei der kurzfristigen Leistungsprognose innerhalb einer Laufkraftwerkskette C. Klingler et al. 10.1007/s00506-022-00849-6
36. Controls on the Spatial and Temporal Patterns of Rainfall‐Runoff Event Characteristics—A Large Sample of Catchments Across Great Britain Y. Zheng et al. 10.1029/2022WR033226
37. Simulating block-scale flood inundation and streamflow using the WRF-Hydro model in the New York City metropolitan area B. Kilicarslan & M. Temimi 10.1007/s11069-024-06597-y
38. Green Roof Hydrological Modelling With GRU and LSTM Networks H. Xie et al. 10.1007/s11269-022-03076-6
39. Performance of long short-term memory networks in predicting athlete injury risk H. Tao et al. 10.3233/JCM-247563
40. Long short-term memory models of water quality in inland water environments J. Pyo et al. 10.1016/j.wroa.2023.100207
41. Intercomparing LSTM and RNN to a Conceptual Hydrological Model for a Low-Land River with a Focus on the Flow Duration Curve A. Ley et al. 10.3390/w15030505
42. In Defense of Metrics: Metrics Sufficiently Encode Typical Human Preferences Regarding Hydrological Model Performance M. Gauch et al. 10.1029/2022WR033918
43. Information and disinformation in hydrological data across space: The case of streamflow predictions using machine learning A. Gupta 10.1016/j.ejrh.2023.101607
44. A state-of-the-art review of long short-term memory models with applications in hydrology and water resources Z. Feng et al. 10.1016/j.asoc.2024.112352
45. A Novel Smoothing-Based Deep Learning Time-Series Approach for Daily Suspended Sediment Load Prediction B. Sahoo et al. 10.1007/s11269-023-03552-7
46. Deep learning for monthly rainfall–runoff modelling: a large-sample comparison with conceptual models across Australia S. Clark et al. 10.5194/hess-28-1191-2024
47. A Comprehensive Review of Methods for Hydrological Forecasting Based on Deep Learning X. Zhao et al. 10.3390/w16101407
48. Estimation of base and surface flow using deep neural networks and a hydrologic model in two watersheds of the Chesapeake Bay J. Lee et al. 10.1016/j.jhydrol.2022.128916
49. Nitrate isotopes in catchment hydrology: Insights, ideas and implications for models I. Matiatos et al. 10.1016/j.jhydrol.2023.130326
50. Alternate pathway for regional flood frequency analysis in data-sparse region N. Mangukiya & A. Sharma 10.1016/j.jhydrol.2024.130635
51. Deep Learning for Vegetation Health Forecasting: A Case Study in Kenya T. Lees et al. 10.3390/rs14030698
52. Comparing a long short-term memory (LSTM) neural network with a physically-based hydrological model for streamflow forecasting over a Canadian catchment B. Sabzipour et al. 10.1016/j.jhydrol.2023.130380
53. EWT_Informer: a novel satellite-derived rainfall–runoff model based on informer S. Wang et al. 10.2166/hydro.2023.228
54. Integration of deep learning and improved multi-objective algorithm to optimize reservoir operation for balancing human and downstream ecological needs R. Qiu et al. 10.1016/j.watres.2024.121314
55. Improving LSTM hydrological modeling with spatiotemporal deep learning and multi-task learning: A case study of three mountainous areas on the Tibetan Plateau B. Li et al. 10.1016/j.jhydrol.2023.129401
56. River flooding mechanisms and their changes in Europe revealed by explainable machine learning S. Jiang et al. 10.5194/hess-26-6339-2022
57. Enhancing process-based hydrological models with embedded neural networks: A hybrid approach B. Li et al. 10.1016/j.jhydrol.2023.130107
58. A Comprehensive Assessment of the Hydrological Evolution and Habitat Quality of the Xiangjiang River Basin F. Hong et al. 10.3390/w15203626
59. Application of Rainfall-Runoff Simulation Based on the NARX Dynamic Neural Network Model Y. Shao et al. 10.3390/w14132082
60. Exploring the potential of deep learning for streamflow forecasting: A comparative study with hydrological models for seasonal and perennial rivers A. Izadi et al. 10.1016/j.eswa.2024.124139
61. How can we benefit from regime information to make more effective use of long short-term memory (LSTM) runoff models? R. Hashemi et al. 10.5194/hess-26-5793-2022
62. DeepGR4J: A deep learning hybridization approach for conceptual rainfall-runoff modelling A. Kapoor et al. 10.1016/j.envsoft.2023.105831
63. Application of a New Hybrid Deep Learning Model That Considers Temporal and Feature Dependencies in Rainfall–Runoff Simulation F. Zhou et al. 10.3390/rs15051395
64. Hybrid Data-Driven Models for Hydrological Simulation and Projection on the Catchment Scale S. Gharbia et al. 10.3390/su14074037
65. 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
66. 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
67. IHACRES, GR4J and MISD-based multi conceptual-machine learning approach for rainfall-runoff modeling B. Mohammadi et al. 10.1038/s41598-022-16215-1
68. Assessing Hydrological Simulations with Machine Learning and Statistical Models E. Rozos 10.3390/hydrology10020049
69. A stochastic conceptual-data-driven approach for improved hydrological simulations J. Quilty et al. 10.1016/j.envsoft.2022.105326
70. Hybrid forecasting: blending climate predictions with AI models L. Slater et al. 10.5194/hess-27-1865-2023
71. A new seq2seq architecture for hourly runoff prediction using historical rainfall and runoff as input S. Gao et al. 10.1016/j.jhydrol.2022.128099
72. 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
73. Enhancing Hydrological Variable Prediction through Multitask LSTM Models Y. Yan et al. 10.3390/w16152156
74. Benchmarking data-driven rainfall-runoff modeling across 54 catchments in the Yellow River Basin: Overfitting, calibration length, dry frequency J. Jin et al. 10.1016/j.ejrh.2022.101119
75. A data‐driven approach for flood prediction using grid‐based meteorological data Y. Wang et al. 10.1002/hyp.14837
76. Hybrid hydrological modeling for large alpine basins: a semi-distributed approach B. Li et al. 10.5194/hess-28-4521-2024
77. Improving hydrologic models for predictions and process understanding using neural ODEs M. Höge et al. 10.5194/hess-26-5085-2022
78. Applying Machine Learning Methods to Improve Rainfall–Runoff Modeling in Subtropical River Basins H. Yu & Q. Yang 10.3390/w16152199
79. A Framework for Estimating the Probability Distribution of Event Runoff Coefficient in Ungauged Catchments Y. Zheng et al. 10.1029/2022WR033227
80. Comparative analysis of rainfall-runoff simulation using a long short-term memory (LSTM) deep learning model and a conceptual hydrological model, HEC-HMS: a case study of the mountainous river basin of Nepal U. Marasini & M. Pokhrel 10.1007/s44290-024-00084-w
81. Hydrological concept formation inside long short-term memory (LSTM) networks T. Lees et al. 10.5194/hess-26-3079-2022
82. How to enhance hydrological predictions in hydrologically distinct watersheds of the Indian subcontinent? N. Mangukiya et al. 10.1002/hyp.14936
83. A hybrid rainfall-runoff model: integrating initial loss and LSTM for improved forecasting W. Wang et al. 10.3389/fenvs.2023.1261239
84. 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
85. A framework on utilizing of publicly availability stream gauges datasets and deep learning in estimating monthly basin-scale runoff in ungauged regions M. Le et al. 10.1016/j.advwatres.2024.104694
86. A large dataset of fluvial hydraulic and geometry attributes derived from USGS field measurement records S. Erfani et al. 10.1016/j.envsoft.2024.106136
87. Can transfer learning improve hydrological predictions in the alpine regions? Y. Yao et al. 10.1016/j.jhydrol.2023.130038
88. Leveraging historic streamflow and weather data with deep learning for enhanced streamflow predictions C. Schutte et al. 10.2166/hydro.2024.268
89. Comprehensive Analysis for Long-Term Hydrological Simulation by Deep Learning Techniques and Remote Sensing C. Xu et al. 10.3389/feart.2022.875145
90. Integration of Generative-Adversarial-Network-Based Data Compaction and Spatial Attention Transductive Long Short-Term Memory for Improved Rainfall–Runoff Modeling B. Ghanati & J. Serra-Sagristà 10.3390/rs16203889
91. Hourly rainfall-runoff modelling by combining the conceptual model with machine learning models in mostly karst Ljubljanica River catchment in Slovenia C. Sezen & M. Šraj 10.1007/s00477-023-02607-w
92. Evaluation and Interpretation of Runoff Forecasting Models Based on Hybrid Deep Neural Networks X. Yang et al. 10.1007/s11269-023-03731-6
93. Development of a One-Parameter New Exponential (ONE) Model for Simulating Rainfall-Runoff and Comparison with Data-Driven LSTM Model J. Lee & J. Noh 10.3390/w15061036
94. Assessment of monthly runoff simulations based on a physics-informed machine learning framework: The effect of intermediate variables in its construction C. Deng et al. 10.1016/j.jenvman.2024.121299
95. Interpretable baseflow segmentation and prediction based on numerical experiments and deep learning Q. Yu et al. 10.1016/j.jenvman.2024.121089
96. Modeling of daily groundwater level using deep learning neural networks M. OTHMAN 10.31127/tuje.1169908
97. Integrating geographic data and the SCS-CN method with LSTM networks for enhanced runoff forecasting in a complex mountain basin M. Merizalde et al. 10.3389/frwa.2023.1233899
98. Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models T. Lees et al. 10.5194/hess-25-5517-2021
99. NeuralHydrology — A Python library for Deep Learning research in hydrology F. Kratzert et al. 10.21105/joss.04050
100. 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