73 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
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4. Runoff simulation driven by multi-source satellite data based on hydrological mechanism algorithm and deep learning network C. Yu et al. 10.1016/j.ejrh.2024.101720
5. 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
6. 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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10. Physics-informed machine learning for understanding rock moisture dynamics in a sandstone cave K. Ouyang et al. 10.5194/hess-27-2579-2023
11. Deep learning-based algorithms for long-term prediction of chlorophyll-a in catchment streams A. Abbas et al. 10.1016/j.jhydrol.2023.130240
12. 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
13. 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
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17. Machine Learning in Assessing the Performance of Hydrological Models E. Rozos et al. 10.3390/hydrology9010005
18. 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
19. 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
20. 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
21. RR-Former: Rainfall-runoff modeling based on Transformer H. Yin et al. 10.1016/j.jhydrol.2022.127781
22. Potenzial von Machine Learning bei der kurzfristigen Leistungsprognose innerhalb einer Laufkraftwerkskette C. Klingler et al. 10.1007/s00506-022-00849-6
23. 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
24. 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
25. Green Roof Hydrological Modelling With GRU and LSTM Networks H. Xie et al. 10.1007/s11269-022-03076-6
26. Long short-term memory models of water quality in inland water environments J. Pyo et al. 10.1016/j.wroa.2023.100207
27. 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
28. In Defense of Metrics: Metrics Sufficiently Encode Typical Human Preferences Regarding Hydrological Model Performance M. Gauch et al. 10.1029/2022WR033918
29. Information and disinformation in hydrological data across space: The case of streamflow predictions using machine learning A. Gupta 10.1016/j.ejrh.2023.101607
30. 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
31. 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
32. 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
33. Nitrate isotopes in catchment hydrology: Insights, ideas and implications for models I. Matiatos et al. 10.1016/j.jhydrol.2023.130326
34. Alternate pathway for regional flood frequency analysis in data-sparse region N. Mangukiya & A. Sharma 10.1016/j.jhydrol.2024.130635
35. Deep Learning for Vegetation Health Forecasting: A Case Study in Kenya T. Lees et al. 10.3390/rs14030698
36. 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
37. EWT_Informer: a novel satellite-derived rainfall–runoff model based on informer S. Wang et al. 10.2166/hydro.2023.228
38. 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
39. 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
40. River flooding mechanisms and their changes in Europe revealed by explainable machine learning S. Jiang et al. 10.5194/hess-26-6339-2022
41. Enhancing process-based hydrological models with embedded neural networks: A hybrid approach B. Li et al. 10.1016/j.jhydrol.2023.130107
42. A Comprehensive Assessment of the Hydrological Evolution and Habitat Quality of the Xiangjiang River Basin F. Hong et al. 10.3390/w15203626
43. Application of Rainfall-Runoff Simulation Based on the NARX Dynamic Neural Network Model Y. Shao et al. 10.3390/w14132082
44. 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
45. DeepGR4J: A deep learning hybridization approach for conceptual rainfall-runoff modelling A. Kapoor et al. 10.1016/j.envsoft.2023.105831
46. 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
47. Hybrid Data-Driven Models for Hydrological Simulation and Projection on the Catchment Scale S. Gharbia et al. 10.3390/su14074037
48. 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
49. 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
50. IHACRES, GR4J and MISD-based multi conceptual-machine learning approach for rainfall-runoff modeling B. Mohammadi et al. 10.1038/s41598-022-16215-1
51. Assessing Hydrological Simulations with Machine Learning and Statistical Models E. Rozos 10.3390/hydrology10020049
52. A stochastic conceptual-data-driven approach for improved hydrological simulations J. Quilty et al. 10.1016/j.envsoft.2022.105326
53. Hybrid forecasting: blending climate predictions with AI models L. Slater et al. 10.5194/hess-27-1865-2023
54. 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
55. 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
56. A data‐driven approach for flood prediction using grid‐based meteorological data Y. Wang et al. 10.1002/hyp.14837
57. Improving hydrologic models for predictions and process understanding using neural ODEs M. Höge et al. 10.5194/hess-26-5085-2022
58. A Framework for Estimating the Probability Distribution of Event Runoff Coefficient in Ungauged Catchments Y. Zheng et al. 10.1029/2022WR033227
59. Hydrological concept formation inside long short-term memory (LSTM) networks T. Lees et al. 10.5194/hess-26-3079-2022
60. How to enhance hydrological predictions in hydrologically distinct watersheds of the Indian subcontinent? N. Mangukiya et al. 10.1002/hyp.14936
61. A hybrid rainfall-runoff model: integrating initial loss and LSTM for improved forecasting W. Wang et al. 10.3389/fenvs.2023.1261239
62. 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
63. 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
64. Can transfer learning improve hydrological predictions in the alpine regions? Y. Yao et al. 10.1016/j.jhydrol.2023.130038
65. Comprehensive Analysis for Long-Term Hydrological Simulation by Deep Learning Techniques and Remote Sensing C. Xu et al. 10.3389/feart.2022.875145
66. 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
67. Evaluation and Interpretation of Runoff Forecasting Models Based on Hybrid Deep Neural Networks X. Yang et al. 10.1007/s11269-023-03731-6
68. 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
69. Modeling of daily groundwater level using deep learning neural networks M. OTHMAN 10.31127/tuje.1169908
70. 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
71. 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
72. NeuralHydrology — A Python library for Deep Learning research in hydrology F. Kratzert et al. 10.21105/joss.04050
73. 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