66 citations as recorded by crossref.

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25. 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
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28. 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
29. 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
30. 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
31. Application of Deep Learning in Drainage Systems Monitoring Data Repair—A Case Study Using Con-GRU Model L. He et al. 10.3390/w15081635
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33. 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
34. 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
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39. 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
40. Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling K. Jafarzadegan et al. 10.1029/2022RG000788
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42. 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
43. Machine learning-enabled calibration of river routing model parameters Y. Zhao et al. 10.2166/hydro.2023.030
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46. Assessing Objective Functions in Streamflow Prediction Model Training Based on the Naïve Method Y. Lin et al. 10.3390/w16050777
47. Bayesian model averaging by combining deep learning models to improve lake water level prediction G. Li et al. 10.1016/j.scitotenv.2023.167718
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50. 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
51. Improving short-term streamflow forecasting by flow mode clustering S. Liu et al. 10.1007/s00477-022-02367-z
52. Operational low-flow forecasting using LSTMs J. Deng et al. 10.3389/frwa.2023.1332678
53. Unpacking the black box of deep learning for identifying El Niño-Southern oscillation Y. Sun et al. 10.1088/1572-9494/ace17d
54. Long short‐term memory model for predicting groundwater level in Alabama V. Robinson et al. 10.1111/1752-1688.13170
55. 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
56. 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
57. Hydrological concept formation inside long short-term memory (LSTM) networks T. Lees et al. 10.5194/hess-26-3079-2022
58. Assessing the Physical Realism of Deep Learning Hydrologic Model Projections Under Climate Change S. Wi & S. Steinschneider 10.1029/2022WR032123
59. The Possible Incoming Runoff Under Extreme Rainfall Event in the Fenhe River Basin S. Jian et al. 10.3389/fenvs.2022.812351
60. 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
61. Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C. Varadharajan et al. 10.1002/hyp.14565
62. 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
63. Closing in on Hydrologic Predictive Accuracy: Combining the Strengths of High‐Fidelity and Physics‐Agnostic Models V. Tran et al. 10.1029/2023GL104464
64. 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
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66. Sandtank-ML: An Educational Tool at the Interface of Hydrology and Machine Learning L. Gallagher et al. 10.3390/w13233328