397 citations as recorded by crossref.

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48. Integrating Satellite Imagery and Ground-Based Measurements with a Machine Learning Model for Monitoring Lake Dynamics over a Semi-Arid Region K. Ekpetere et al. 10.3390/hydrology10040078
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51. A Comparative Study of a Two-Dimensional Slope Hydrodynamic Model (TDSHM), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) Models for Runoff Prediction Y. Zhou et al. 10.3390/w17091380
52. Basin-informed flood frequency analysis using deep learning exhibits consistent projected regional patterns over CONUS R. Lazin et al. 10.1038/s41598-025-97610-2
53. Sustainable water allocation under climate change: Deep learning approaches to predict drinking water shortages H. Moghaddam et al. 10.1016/j.jenvman.2025.125600
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55. Caravan - A global community dataset for large-sample hydrology F. Kratzert et al. 10.1038/s41597-023-01975-w
56. Profiling and Pairing Catchments and Hydrological Models With Latent Factor Model Y. Yang & T. Chui 10.1029/2022WR033684
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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. 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
62. Classification-enhanced LSTM model for predicting river water levels J. Luo et al. 10.1016/j.jhydrol.2024.132535
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64. Improving the Accuracy of Dam Inflow Predictions Using a Long Short-Term Memory Network Coupled with Wavelet Transform and Predictor Selection T. Tran et al. 10.3390/math9050551
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79. Closing in on Hydrologic Predictive Accuracy: Combining the Strengths of High‐Fidelity and Physics‐Agnostic Models V. Tran et al. 10.1029/2023GL104464
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82. 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
83. Hydrological Response of the Irrawaddy River Under Climate Change Based on CV-LSTM Model X. Luo et al. 10.3390/w17040479
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305. LamaH-Ice: LArge-SaMple DAta for Hydrology and Environmental Sciences for Iceland H. Helgason & B. Nijssen 10.5194/essd-16-2741-2024
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308. In Defense of Metrics: Metrics Sufficiently Encode Typical Human Preferences Regarding Hydrological Model Performance M. Gauch et al. 10.1029/2022WR033918
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344. What Role Does Hydrological Science Play in the Age of Machine Learning? G. Nearing et al. 10.1029/2020WR028091
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346. Influence of cascade reservoir operation in the Upper Mekong River on the general hydrological regime: A combined data-driven modeling approach X. Yuan et al. 10.1016/j.jenvman.2022.116339
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348. Development of a Long-Range Hydrological Drought Prediction Framework Using Deep Learning M. Khan & R. Maity 10.1007/s11269-024-03735-w
349. 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
350. Long short-term memory integrating moving average method for flood inundation depth forecasting based on observed data in urban area S. Yang et al. 10.1007/s11069-022-05766-1
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355. 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
356. Hybrid deep learning approach for multi-step-ahead prediction for daily maximum temperature and heatwaves M. Khan & R. Maity 10.1007/s00704-022-04103-7
357. Assessment of drought conditions and prediction by machine learning algorithms using Standardized Precipitation Index and Standardized Water-Level Index (case study: Yazd province, Iran) R. Shakeri et al. 10.1007/s11356-023-29522-5
358. Technical Note: The divide and measure nonconformity – how metrics can mislead when we evaluate on different data partitions D. Klotz et al. 10.5194/hess-28-3665-2024
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