97 citations as recorded by crossref.

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6. River flooding mechanisms and their changes in Europe revealed by explainable machine learning S. Jiang et al. 10.5194/hess-26-6339-2022
7. Application of Machine Learning Algorithms in Predicting Extreme Rainfall Events in Rwanda J. Kagabo et al. 10.3390/atmos15060691
8. Bias learning improves data driven models for streamflow prediction Y. Lin et al. 10.1016/j.ejrh.2023.101557
9. The limitation of machine learning methods for water supply and demand forecasting: A case study for Greater Melbourne, Australia M. Mohammadi et al. 10.2166/ws.2024.225
10. Machine Learning-Based Flood Forecasting System for Window Cliffs State Natural Area, Tennessee G. Darkwah et al. 10.3390/geohazards5010004
11. 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
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19. Improving Sub-daily Runoff Forecast Based on the Multi-objective Optimized Extreme Learning Machine for Reservoir Operation W. Jia et al. 10.1007/s11269-024-03953-2
20. Development of objective function-based ensemble model for streamflow forecasts Y. Lin et al. 10.1016/j.jhydrol.2024.130861
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22. A review of hybrid deep learning applications for streamflow forecasting K. Ng et al. 10.1016/j.jhydrol.2023.130141
23. Ensemble learning of catchment-wise optimized LSTMs enhances regional rainfall-runoff modelling − case Study: Basque Country, Spain F. Hosseini et al. 10.1016/j.jhydrol.2024.132269
24. LandBench 1.0: A benchmark dataset and evaluation metrics for data-driven land surface variables prediction Q. Li et al. 10.1016/j.eswa.2023.122917
25. How accurate are the machine learning models in improving monthly rainfall prediction in hyper arid environment? F. Baig et al. 10.1016/j.jhydrol.2024.131040
26. From risk control to resilience: developments and trends of urban roads designed as surface flood passages to cope with extreme storms Z. Shao et al. 10.1007/s11783-024-1782-9
27. A data-centric perspective on the information needed for hydrological uncertainty predictions A. Auer et al. 10.5194/hess-28-4099-2024
28. Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models R. Arsenault et al. 10.5194/hess-27-139-2023
29. How extreme hydrological events correspond to climate extremes in the context of global warming: A case study in the Luanhe River Basin of North China G. Gao et al. 10.1002/joc.8459
30. Streamflow prediction using machine learning models in selected rivers of Southern India R. Sharma et al. 10.1080/15715124.2023.2196635
31. Assessing the simulation of streamflow with the LSTM model across the continental United States using the MOPEX dataset A. Tounsi et al. 10.1007/s00521-023-08922-1
32. Sensitivity analysis of regional rainfall-induced landslide based on UAV photogrammetry and LSTM neural network L. Zhao et al. 10.1007/s11629-023-7991-z
33. Runoff Simulation in Data-Scarce Alpine Regions: Comparative Analysis Based on LSTM and Physically Based Models J. Yue et al. 10.3390/w16152161
34. Enhancing hydrological modeling with transformers: a case study for 24-h streamflow prediction B. Demiray et al. 10.2166/wst.2024.110
35. Hyperparameter optimization of regional hydrological LSTMs by random search: A case study from Basque Country, Spain F. Hosseini et al. 10.1016/j.jhydrol.2024.132003
36. 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
37. High flow prediction model integrating physically and deep learning based approaches with quasi real-time watershed data assimilation M. Jeong et al. 10.1016/j.jhydrol.2024.131304
38. 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
39. Data-Driven Dam Outflow Prediction Using Deep Learning with Simultaneous Selection of Input Predictors and Hyperparameters Using the Bayesian Optimization Algorithm V. Tran et al. 10.1007/s11269-023-03677-9
40. Machine learning applications for weather and climate need greater focus on extremes P. Watson 10.1088/1748-9326/ac9d4e
41. A comparative study of daily streamflow forecasting using firefly, artificial bee colony, and genetic algorithm-based artificial neural network H. Kilinc et al. 10.1007/s11600-024-01362-y
42. Predicting streamflow with LSTM networks using global datasets K. Wilbrand et al. 10.3389/frwa.2023.1166124
43. Multisite evaluation of physics-informed deep learning for permafrost prediction in the Qinghai-Tibet Plateau Y. Liu et al. 10.1016/j.coldregions.2023.104009
44. A new stable and interpretable flood forecasting model combining multi-head attention mechanism and multiple linear regression Y. Wang et al. 10.2166/hydro.2023.160
45. 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
46. The effectiveness of machine learning methods in the nonlinear coupled data assimilation Z. Xuan et al. 10.1186/s40562-024-00347-5
47. Hydrologic connectivity and dynamics of solute transport in a mountain stream: Insights from a long-term tracer test and multiscale transport modeling informed by machine learning P. Le et al. 10.1016/j.jhydrol.2024.131562
48. Application of Deep Learning in Drainage Systems Monitoring Data Repair—A Case Study Using Con-GRU Model L. He et al. 10.3390/w15081635
49. ER-MACG: An Extreme Precipitation Forecasting Model Integrating Self-Attention Based on FY4A Satellite Data M. Lu et al. 10.3390/rs16203911
50. 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
51. APPLICATION OF DEEP LEARNING TO DAM INFLOW FORECASTS WITH DIFFERENT CHARACTERISTICS IN THE CHUBU AREA AND THE EFFECT OF FORECAST ACCURACY CAUSED BY MIX OF INPUT RAINFALL TYPES T. KUREBAYASHI et al. 10.2208/jscejj.23-16182
52. Reconstruction of missing streamflow series in human-regulated catchments using a data integration LSTM model A. Tursun et al. 10.1016/j.ejrh.2024.101744
53. Long short-term memory models to quantify long-term evolution of streamflow discharge and groundwater depth in Alabama H. Gholizadeh et al. 10.1016/j.scitotenv.2023.165884
54. Advancement of a Blended Hydrologic Model for Robust Model Performance R. Chlumsky et al. 10.1061/JHYEFF.HEENG-6246
55. Remote sensing-enabled machine learning for river water quality modeling under multidimensional uncertainty S. Rahat et al. 10.1016/j.scitotenv.2023.165504
56. Streamflow forecast model using ANN D. Sonowal et al. 10.1080/23249676.2024.2310098
57. Multiple data-driven approaches for estimating daily streamflow in the Kone River basin, Vietnam T. Thach 10.1007/s12145-024-01390-8
58. Global prediction of extreme floods in ungauged watersheds G. Nearing et al. 10.1038/s41586-024-07145-1
59. Generalization of an Encoder-Decoder LSTM model for flood prediction in ungauged catchments Y. Zhang et al. 10.1016/j.jhydrol.2022.128577
60. 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
61. Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling K. Jafarzadegan et al. 10.1029/2022RG000788
62. A compound approach for ten-day runoff prediction by coupling wavelet denoising, attention mechanism, and LSTM based on GPU parallel acceleration technology Y. Wang et al. 10.1007/s12145-023-01212-3
63. A Comprehensive Review of Methods for Hydrological Forecasting Based on Deep Learning X. Zhao et al. 10.3390/w16101407
64. Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States K. Hunt et al. 10.5194/hess-26-5449-2022
65. Machine learning-enabled calibration of river routing model parameters Y. Zhao et al. 10.2166/hydro.2023.030
66. Deep Convolutional LSTM for improved flash flood prediction P. Oddo et al. 10.3389/frwa.2024.1346104
67. Improving streamflow predictions across CONUS by integrating advanced machine learning models and diverse data K. Tayal et al. 10.1088/1748-9326/ad6fb7
68. Flood forecasting with machine learning models in an operational framework S. Nevo et al. 10.5194/hess-26-4013-2022
69. A New Data-Driven Model to Predict Monthly Runoff at Watershed Scale: Insights from Deep Learning Method Applied in Data-Driven Model S. Jia et al. 10.1007/s11269-024-03907-8
70. Assessing Objective Functions in Streamflow Prediction Model Training Based on the Naïve Method Y. Lin et al. 10.3390/w16050777
71. Bayesian model averaging by combining deep learning models to improve lake water level prediction G. Li et al. 10.1016/j.scitotenv.2023.167718
72. Hydro-informer: a deep learning model for accurate water level and flood predictions W. Almikaeel et al. 10.1007/s11069-024-06949-8
73. Assessing rainfall prediction models: Exploring the advantages of machine learning and remote sensing approaches S. Latif et al. 10.1016/j.aej.2023.09.060
74. Hybrid forecasting: blending climate predictions with AI models L. Slater et al. 10.5194/hess-27-1865-2023
75. 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
76. Improving short-term streamflow forecasting by flow mode clustering S. Liu et al. 10.1007/s00477-022-02367-z
77. Operational low-flow forecasting using LSTMs J. Deng et al. 10.3389/frwa.2023.1332678
78. Unpacking the black box of deep learning for identifying El Niño-Southern oscillation Y. Sun et al. 10.1088/1572-9494/ace17d
79. Long short‐term memory model for predicting groundwater level in Alabama V. Robinson et al. 10.1111/1752-1688.13170
80. Deep learning of estuary salinity dynamics is physically accurate at a fraction of hydrodynamic model computational cost G. Gorski et al. 10.1002/lno.12549
81. Use of one-dimensional CNN for input data size reduction in LSTM for improved computational efficiency and accuracy in hourly rainfall-runoff modeling K. Ishida et al. 10.1016/j.jenvman.2024.120931
82. Hybrid Deep Learning Time-Lagged Precipitation-Runoff Model T. Calvette et al. 10.1061/JHYEFF.HEENG-6222
83. Forecasting High-Flow Discharges in a Flashy Catchment Using Multiple Precipitation Estimates as Predictors in Machine Learning Models A. Zanchetta et al. 10.3390/hydrology9120216
84. Linking explainable artificial intelligence and soil moisture dynamics in a machine learning streamflow model A. Ley et al. 10.2166/nh.2024.003
85. Fluvial Dynamics and Hydrological Variability in the Chiriquí Viejo River Basin, Panama: An Assessment of Hydro-Social Sustainability through Advanced Hydrometric Indexes H. De Gracia et al. 10.3390/w16121662
86. Physics-enhanced machine learning models for streamflow discharge forecasting Y. Zhao et al. 10.2166/hydro.2024.061
87. Hydrological concept formation inside long short-term memory (LSTM) networks T. Lees et al. 10.5194/hess-26-3079-2022
88. Assessing the Physical Realism of Deep Learning Hydrologic Model Projections Under Climate Change S. Wi & S. Steinschneider 10.1029/2022WR032123
89. The Possible Incoming Runoff Under Extreme Rainfall Event in the Fenhe River Basin S. Jian et al. 10.3389/fenvs.2022.812351
90. Interpreting Deep Machine Learning for Streamflow Modeling Across Glacial, Nival, and Pluvial Regimes in Southwestern Canada S. Anderson & V. Radić 10.3389/frwa.2022.934709
91. EVALUATION OF THE APPLICABILITY OF DATA AUGMENTATION FOR DAM INFLOW PREDICTION USING DEEP LEARNING M. HITOKOTO et al. 10.2208/journalofjsce.24-00029
92. Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C. Varadharajan et al. 10.1002/hyp.14565
93. EVALUATION OF APPLICABILITY OF DATA AUGMENTATION METHOD FOR DAM INFLOW PREDICTION USING DEEP LEARNING M. HITOKOTO et al. 10.2208/jscejhe.78.2_I_175
94. Closing in on Hydrologic Predictive Accuracy: Combining the Strengths of High‐Fidelity and Physics‐Agnostic Models V. Tran et al. 10.1029/2023GL104464
95. Robust and efficient uncertainty quantification for extreme events that deviate significantly from the training dataset using polynomial chaos-kriging V. Tran & J. Kim 10.1016/j.jhydrol.2022.127716
96. Data reformation – A novel data processing technique enhancing machine learning applicability for predicting streamflow extremes V. Tran et al. 10.1016/j.advwatres.2023.104569
97. Sandtank-ML: An Educational Tool at the Interface of Hydrology and Machine Learning L. Gallagher et al. 10.3390/w13233328