663 citations as recorded by crossref.

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40. 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
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44. Prediction of Glacially Derived Runoff in the Muzati River Watershed Based on the PSO-LSTM Model X. Yang et al. 10.3390/w14132018
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46. Predicting mine water inrush accidents based on water level anomalies of borehole groups using long short-term memory and isolation forest H. Yin et al. 10.1016/j.jhydrol.2022.128813
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48. Wireless Telecommunication Links for Rainfall Monitoring: Deep Learning Approach and Experimental Results F. Diba et al. 10.1109/ACCESS.2021.3076781
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50. Stream Temperature Predictions for River Basin Management in the Pacific Northwest and Mid-Atlantic Regions Using Machine Learning H. Weierbach et al. 10.3390/w14071032
51. Development of a Deep Learning-Based Prediction Model for Water Consumption at the Household Level J. Kim et al. 10.3390/w14091512
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54. A real‐time flood forecasting hybrid machine learning hydrological model for Krong H'nang hydropower reservoir P. Nguyen et al. 10.1002/rvr2.72
55. Short-term Lake Erie algal bloom prediction by classification and regression models H. Ai et al. 10.1016/j.watres.2023.119710
56. Machine learning models coupled with empirical mode decomposition for simulating monthly and yearly streamflows: a case study of three watersheds in Ontario, Canada P. Parisouj et al. 10.1080/19942060.2023.2242445
57. Predicting the Overflowing of Urban Personholes Based on Machine Learning Techniques Y. Chang et al. 10.3390/w15234100
58. Developing data-driven learning models to predict urban stormwater runoff volume R. Wood-Ponce et al. 10.1080/1573062X.2024.2312514
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61. 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
62. Performance of statistical and machine learning ensembles for daily temperature downscaling X. Li et al. 10.1007/s00704-020-03098-3
63. EMULATION EVALUATION OF URBAN RUNOFF MODEL BY DEEP LEARNING FOR THE VIRTUAL HYDROGRAPH WITH OBSERVATION NOISE S. FUJIZUKA et al. 10.2208/jscejer.76.5_I_383
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66. Cause-driven Streamflow Forecasting Framework Based on Linear Correlation Reconstruction and Long Short-term Memory Y. Lian et al. 10.1007/s11269-022-03097-1
67. Assessing the Physical Realism of Deep Learning Hydrologic Model Projections Under Climate Change S. Wi & S. Steinschneider 10.1029/2022WR032123
68. Regionalization in a Global Hydrologic Deep Learning Model: From Physical Descriptors to Random Vectors X. Li et al. 10.1029/2021WR031794
69. 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
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71. Improving the estimation of atmospheric water vapor pressure using interpretable long short-term memory networks B. Gao et al. 10.1016/j.agrformet.2024.109907
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74. Lion Algorithm-Optimized Long Short-Term Memory Network for Groundwater Level Forecasting in Udupi District, India B. Supreetha et al. 10.1155/2020/8685724
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77. Runoff Prediction Based on the Discharge of Pump Stations in an Urban Stream Using a Modified Multi-Layer Perceptron Combined with Meta-Heuristic Optimization W. Lee & E. Lee 10.3390/w14010099
78. A framework to assess multi-hazard physical climate risk for power generation projects from publicly-accessible sources T. Luo et al. 10.1038/s43247-023-00782-w
79. Structural-optimized sequential deep learning methods for surface soil moisture forecasting, case study Quebec, Canada M. Zeynoddin & H. Bonakdari 10.1007/s00521-022-07529-2
80. 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
81. Advancing stream classification and hydrologic modeling of ungaged basins for environmental flow management in coastal southern California S. Adams et al. 10.5194/hess-27-3021-2023
82. A Physics-Informed, Machine Learning Emulator of a 2D Surface Water Model: What Temporal Networks and Simulation-Based Inference Can Help Us Learn about Hydrologic Processes R. Maxwell et al. 10.3390/w13243633
83. Prediction model of sparse autoencoder-based bidirectional LSTM for wastewater flow rate J. Huang et al. 10.1007/s11227-022-04827-3
84. Hybrid short-term runoff prediction model based on optimal variational mode decomposition, improved Harris hawks algorithm and long short-term memory network W. Sun et al. 10.1088/2515-7620/ac5feb
85. Improving the simulations of the hydrological model in the karst catchment by integrating the conceptual model with machine learning models C. Sezen & M. Šraj 10.1016/j.scitotenv.2024.171684
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