81 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. 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
7. Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for hydrological processes P. Bhasme et al. 10.1016/j.jhydrol.2022.128618
8. Multi-model ensemble benchmark data for hydrological modeling in Japanese river basins Y. Sawada et al. 10.3178/hrl.16.73
9. The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL) J. Mai et al. 10.5194/hess-26-3537-2022
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. Linking explainable artificial intelligence and soil moisture dynamics in a machine learning streamflow model A. Ley et al. 10.2166/nh.2024.003
12. Deep learning-based algorithms for long-term prediction of chlorophyll-a in catchment streams A. Abbas et al. 10.1016/j.jhydrol.2023.130240
13. 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
14. 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
15. Predicting streamflow with LSTM networks using global datasets K. Wilbrand et al. 10.3389/frwa.2023.1166124
16. Generalization of an Encoder-Decoder LSTM model for flood prediction in ungauged catchments Y. Zhang et al. 10.1016/j.jhydrol.2022.128577
17. Broadleaf afforestation impacts on terrestrial hydrology insignificant compared to climate change in Great Britain M. Buechel et al. 10.5194/hess-28-2081-2024
18. Runoff predictions in new-gauged basins using two transformer-based models H. Yin et al. 10.1016/j.jhydrol.2023.129684
19. Machine Learning in Assessing the Performance of Hydrological Models E. Rozos et al. 10.3390/hydrology9010005
20. 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
21. 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
22. Integrating Euclidean and non-Euclidean spatial information for deep learning-based spatiotemporal hydrological simulation L. Deng et al. 10.1016/j.jhydrol.2024.131438
23. 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
24. RR-Former: Rainfall-runoff modeling based on Transformer H. Yin et al. 10.1016/j.jhydrol.2022.127781
25. Potenzial von Machine Learning bei der kurzfristigen Leistungsprognose innerhalb einer Laufkraftwerkskette C. Klingler et al. 10.1007/s00506-022-00849-6
26. 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
27. 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
28. Green Roof Hydrological Modelling With GRU and LSTM Networks H. Xie et al. 10.1007/s11269-022-03076-6
29. Long short-term memory models of water quality in inland water environments J. Pyo et al. 10.1016/j.wroa.2023.100207
30. 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
31. In Defense of Metrics: Metrics Sufficiently Encode Typical Human Preferences Regarding Hydrological Model Performance M. Gauch et al. 10.1029/2022WR033918
32. Information and disinformation in hydrological data across space: The case of streamflow predictions using machine learning A. Gupta 10.1016/j.ejrh.2023.101607
33. 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
34. 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
35. A Comprehensive Review of Methods for Hydrological Forecasting Based on Deep Learning X. Zhao et al. 10.3390/w16101407
36. 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
37. Nitrate isotopes in catchment hydrology: Insights, ideas and implications for models I. Matiatos et al. 10.1016/j.jhydrol.2023.130326
38. Alternate pathway for regional flood frequency analysis in data-sparse region N. Mangukiya & A. Sharma 10.1016/j.jhydrol.2024.130635
39. Deep Learning for Vegetation Health Forecasting: A Case Study in Kenya T. Lees et al. 10.3390/rs14030698
40. 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
41. EWT_Informer: a novel satellite-derived rainfall–runoff model based on informer S. Wang et al. 10.2166/hydro.2023.228
42. 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
43. 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
44. River flooding mechanisms and their changes in Europe revealed by explainable machine learning S. Jiang et al. 10.5194/hess-26-6339-2022
45. Enhancing process-based hydrological models with embedded neural networks: A hybrid approach B. Li et al. 10.1016/j.jhydrol.2023.130107
46. A Comprehensive Assessment of the Hydrological Evolution and Habitat Quality of the Xiangjiang River Basin F. Hong et al. 10.3390/w15203626
47. Application of Rainfall-Runoff Simulation Based on the NARX Dynamic Neural Network Model Y. Shao et al. 10.3390/w14132082
48. 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
49. 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
50. DeepGR4J: A deep learning hybridization approach for conceptual rainfall-runoff modelling A. Kapoor et al. 10.1016/j.envsoft.2023.105831
51. 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
52. Hybrid Data-Driven Models for Hydrological Simulation and Projection on the Catchment Scale S. Gharbia et al. 10.3390/su14074037
53. 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
54. 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
55. IHACRES, GR4J and MISD-based multi conceptual-machine learning approach for rainfall-runoff modeling B. Mohammadi et al. 10.1038/s41598-022-16215-1
56. Assessing Hydrological Simulations with Machine Learning and Statistical Models E. Rozos 10.3390/hydrology10020049
57. A stochastic conceptual-data-driven approach for improved hydrological simulations J. Quilty et al. 10.1016/j.envsoft.2022.105326
58. Hybrid forecasting: blending climate predictions with AI models L. Slater et al. 10.5194/hess-27-1865-2023
59. 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
60. 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
61. A data‐driven approach for flood prediction using grid‐based meteorological data Y. Wang et al. 10.1002/hyp.14837
62. Improving hydrologic models for predictions and process understanding using neural ODEs M. Höge et al. 10.5194/hess-26-5085-2022
63. A Framework for Estimating the Probability Distribution of Event Runoff Coefficient in Ungauged Catchments Y. Zheng et al. 10.1029/2022WR033227
64. Hydrological concept formation inside long short-term memory (LSTM) networks T. Lees et al. 10.5194/hess-26-3079-2022
65. How to enhance hydrological predictions in hydrologically distinct watersheds of the Indian subcontinent? N. Mangukiya et al. 10.1002/hyp.14936
66. A hybrid rainfall-runoff model: integrating initial loss and LSTM for improved forecasting W. Wang et al. 10.3389/fenvs.2023.1261239
67. 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
68. 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
69. Can transfer learning improve hydrological predictions in the alpine regions? Y. Yao et al. 10.1016/j.jhydrol.2023.130038
70. Leveraging historic streamflow and weather data with deep learning for enhanced streamflow predictions C. Schutte et al. 10.2166/hydro.2024.268
71. Comprehensive Analysis for Long-Term Hydrological Simulation by Deep Learning Techniques and Remote Sensing C. Xu et al. 10.3389/feart.2022.875145
72. 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
73. Evaluation and Interpretation of Runoff Forecasting Models Based on Hybrid Deep Neural Networks X. Yang et al. 10.1007/s11269-023-03731-6
74. 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
75. 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
76. Interpretable baseflow segmentation and prediction based on numerical experiments and deep learning Q. Yu et al. 10.1016/j.jenvman.2024.121089
77. Modeling of daily groundwater level using deep learning neural networks M. OTHMAN 10.31127/tuje.1169908
78. 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
79. 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
80. NeuralHydrology — A Python library for Deep Learning research in hydrology F. Kratzert et al. 10.21105/joss.04050
81. 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