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14. Variability of Snow and Rainfall Partitioning Into Evapotranspiration and Summer Runoff Across Nine Mountainous Catchments M. Sprenger et al. 10.1029/2022GL099324
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18. Development and evaluation of the cascade correlation neural network and the random forest models for river stage and river flow prediction in Australia M. Ghorbani et al. 10.1007/s00500-019-04648-2
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20. Development and Evaluation of the Combined Machine Learning Models for the Prediction of Dam Inflow J. Hong et al. 10.3390/w12102927
21. Forest-cover-loss control on year-round river flow dynamics in the upper Saint John River (Wolastoq) basin, Northeastern North America from 2001 to 2019 C. Jin et al. 10.1016/j.jhydrol.2023.129776
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23. Employing Machine Learning Algorithms for Streamflow Prediction: A Case Study of Four River Basins with Different Climatic Zones in the United States P. Parisouj et al. 10.1007/s11269-020-02659-5
24. Temporal-Spatial changes of monthly vegetation growth and their driving forces in the ancient Yellow river irrigation system, China K. Li et al. 10.1016/j.jconhyd.2021.103911
25. Examination and comparison of binary metaheuristic wrapper-based input variable selection for local and global climate information-driven one-step monthly streamflow forecasting K. Ren et al. 10.1016/j.jhydrol.2021.126152
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27. A Medium and Long-Term Runoff Forecast Method Based on Massive Meteorological Data and Machine Learning Algorithms Y. Li et al. 10.3390/w13091308
28. Artificial Intelligence in Hydrology: Advancements in Soil, Water Resource Management, and Sustainable Development S. Biazar et al. 10.3390/su17052250
29. Hydrologic interpretation of machine learning models for 10-daily streamflow simulation in climate sensitive upper Indus catchments H. Mushtaq et al. 10.1007/s00704-024-04932-8
30. Quantifying Climate and Catchment Control on Hydrological Drought in the Continental United States G. Konapala & A. Mishra 10.1029/2018WR024620
31. Performance of Different Crop Models in Simulating Soil Temperature J. Kandasamy et al. 10.3390/s23062891
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33. Geospatial patterns in runoff projections using random forest based forecasting of time-series data for the mid-Atlantic region of the United States B. Gaertner 10.1016/j.scitotenv.2023.169211
34. Comparative assessment of empirical random forest family's model in simulating future streamflow in different basin of Sarawak, Malaysia Z. Sa’adi et al. 10.1016/j.jastp.2024.106381
35. What controls hydrology? An assessment across the contiguous United States through an interpretable machine learning approach K. Li & S. Razavi 10.1016/j.jhydrol.2024.131835
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37. Impact of Uncertainty Parameter Distribution on Robust Decision Making Outcomes for Climate Change Adaptation under Deep Uncertainty J. Reis & J. Shortridge 10.1111/risa.13405
38. Neuroforecasting of daily streamflows in the UK for short- and medium-term horizons: A novel insight F. Granata & F. Di Nunno 10.1016/j.jhydrol.2023.129888
39. Long-Term Groundwater Level Prediction Model Based on Hybrid KNN-RF Technique O. Kombo et al. 10.3390/hydrology7030059
40. Predictive Modeling of Envelope Flood Extents Using Geomorphic and Climatic‐Hydrologic Catchment Characteristics R. Tavares da Costa et al. 10.1029/2019WR026453
41. Streamflow Prediction with Time-Lag-Informed Random Forest and Its Performance Compared to SWAT in Diverse Catchments D. Moges et al. 10.3390/w16192805
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45. A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources H. Tyralis et al. 10.3390/w11050910
46. Streamflow simulation using conceptual and neural network models in the Hemavathi sub-watershed, India N. Reddy et al. 10.1016/j.geogeo.2022.100153
47. On constructing limits-of-acceptability in watershed hydrology using decision trees A. Gupta et al. 10.1016/j.advwatres.2023.104486
48. A hybrid bayesian vine model for water level prediction Z. Liu et al. 10.1016/j.envsoft.2021.105075
49. Reliability Assessment of Machine Learning Models in Hydrological Predictions Through Metamorphic Testing Y. Yang & T. Chui 10.1029/2020WR029471
50. Long-Term Streamflow Forecasting Based on Relevance Vector Machine Model Y. Liu et al. 10.3390/w9010009
51. Newly explored machine learning model for river flow time series forecasting at Mary River, Australia F. Cui et al. 10.1007/s10661-020-08724-1
52. Improving predictions of hydrological low-flow indices in ungaged basins using machine learning S. Worland et al. 10.1016/j.envsoft.2017.12.021
53. Anthropogenic intensification of the eco-hydrothermal regime transition in regulated rivers: The cumulative effect of cascade reservoirs H. Yang et al. 10.1016/j.jenvman.2023.119478
54. Runoff Predictions in a Semiarid Watershed by Convolutional Neural Networks Improved with Metaheuristic Algorithms and Forced with Reanalysis and Climate Data Y. Aoulmi et al. 10.1061/JHYEFF.HEENG-5920
55. Multiple Types of Missing Precipitation Data Filling Based on Ensemble Artificial Intelligence Models H. Qiu et al. 10.3390/w16223192
56. Spatial Patterns of Water Age: Using Young Water Fractions to Improve the Characterization of Transit Times in Contrasting Catchments S. Lutz et al. 10.1029/2017WR022216
57. Modeling groundwater-level responses to multiple stresses using transfer-function models and wavelet analysis in a coastal aquifer system G. Yang & K. McCoy 10.1016/j.jhydrol.2023.130426
58. Prediction of multi-sectoral longitudinal water withdrawals using hierarchical machine learning models J. Shortridge 10.2166/hydro.2023.110
59. Streamflow forecasting in Tocantins river basins using machine learning V. Duarte et al. 10.2166/ws.2022.155
60. Predicting national-scale tile drainage discharge in Denmark using machine learning algorithms S. Motarjemi et al. 10.1016/j.ejrh.2021.100839
61. Predicting Inflow Rate of the Soyang River Dam Using Deep Learning Techniques S. Lee & J. Kim 10.3390/w13172447
62. Hydrologically Informed Machine Learning for Rainfall‐Runoff Modeling: A Genetic Programming‐Based Toolkit for Automatic Model Induction J. Chadalawada et al. 10.1029/2019WR026933
63. A Hybrid Downscaling Approach for Future Temperature and Precipitation Change H. Erlandsen et al. 10.1175/JAMC-D-20-0013.1
64. Rainfall–runoff relationships at event scale in western Mediterranean ephemeral streams R. Serrano-Notivoli et al. 10.5194/hess-26-1243-2022
65. Development of Rainfall-Runoff Models for Sustainable Stormwater Management in Urbanized Catchments B. Szeląg et al. 10.3390/w14131997
66. Evaluating Runoff-Sediment Relationship Variations Using Generalized Additive Models That Incorporate Reservoir Indices for Check Dams L. Li et al. 10.1007/s11269-021-02928-x
67. Robust decision outcomes with induced correlations in climatic and economic parameters J. Reis & J. Shortridge 10.1007/s11027-021-09970-5
68. Towards an efficient streamflow forecasting method for event-scales in Ca River basin, Vietnam X. Le et al. 10.1016/j.ejrh.2023.101328
69. A Statistical Approach to Mapping Flood Susceptibility in the Lower Connecticut River Valley Region J. Giovannettone et al. 10.1029/2018WR023018
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73. River discharge prediction using wavelet-based artificial neural network and long short-term memory models: a case study of Teesta River Basin, India S. Chakraborty & S. Biswas 10.1007/s00477-023-02443-y
74. Comparison of Hydrological Model-Based and Geographical-Based Approaches for Estimating Water Travel Times for Source Water Protection F. St-Pierre et al. 10.1007/s40710-020-00473-0
75. Mapping the temporary and perennial character of whole river networks A. González‐Ferreras & J. Barquín 10.1002/2017WR020390
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77. Boreholes Data Analysis Architecture Based on Clustering and Prediction Models for Enhancing Underground Safety Verification N. Iqbal et al. 10.1109/ACCESS.2021.3083175
78. Improving streamflow prediction in the WRF-Hydro model with LSTM networks K. Cho & Y. Kim 10.1016/j.jhydrol.2021.127297
79. Forecasting of daily streamflows downstream from reservoirs with streamflow regularization using machine learning methods T. Generoso et al. 10.1016/j.jsames.2023.104583
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81. AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods A. Abbas et al. 10.5194/gmd-15-3021-2022
82. Evaluating the performance of random forest for large-scale flood discharge simulation L. Schoppa et al. 10.1016/j.jhydrol.2020.125531
83. Hybrid Data-Driven Models for Hydrological Simulation and Projection on the Catchment Scale S. Gharbia et al. 10.3390/su14074037
84. Impacts of climate change on groundwater level and irrigation cost in a groundwater dependent irrigated region G. Salem et al. 10.1016/j.agwat.2018.06.011
85. Statistical downscaling of precipitation using machine learning techniques D. Sachindra et al. 10.1016/j.atmosres.2018.05.022
86. Predicting and understanding residential water use with interpretable machine learning B. Rachunok et al. 10.1088/1748-9326/ad1434
87. A Nonparametric Statistical Technique for Spatial Downscaling of Precipitation Over High Mountain Asia Y. Mei et al. 10.1029/2020WR027472
88. Use of modeling to map potentially toxic elements and assess the risk to human health in soils affected by mining activity É. de Castro Paes et al. 10.1016/j.catena.2022.106662
89. Daily Streamflow Forecasting Based on the Hybrid Particle Swarm Optimization and Long Short-Term Memory Model in the Orontes Basin H. Kilinc 10.3390/w14030490
90. Scale Effects of the Monthly Streamflow Prediction Using a State-of-the-art Deep Learning Model W. Xu et al. 10.1007/s11269-022-03216-y
91. Using Random Forest, a machine learning approach to predict nitrogen, phosphorus, and sediment event mean concentrations in urban runoff M. Behrouz et al. 10.1016/j.jenvman.2022.115412
92. Simulations of Snowmelt Runoff in a High-Altitude Mountainous Area Based on Big Data and Machine Learning Models: Taking the Xiying River Basin as an Example G. Wang et al. 10.3390/rs15041118
93. Evaluation of random forests and Prophet for daily streamflow forecasting G. Papacharalampous & H. Tyralis 10.5194/adgeo-45-201-2018
94. How well do gridded precipitation and actual evapotranspiration products represent the key water balance components in the Nile Basin? I. McNamara et al. 10.1016/j.ejrh.2021.100884
95. Effect of environmental covariable selection in the hydrological modeling using machine learning models to predict daily streamflow G. Reis et al. 10.1016/j.jenvman.2021.112625
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102. Evaluation of the factors explaining the use of agricultural land: A machine learning and model-agnostic approach C. Viana et al. 10.1016/j.ecolind.2021.108200
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114. Interpreting and modelling the daily extreme sediment events in karst mountain watersheds J. Jing et al. 10.1016/j.scitotenv.2024.171956
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116. Neural network and cubist algorithms to predict fecal coliform content in treated wastewater by multi‐soil‐layering system for potential reuse S. Sbahi et al. 10.1002/jeq2.20176
117. Evaluating different machine learning methods to simulate runoff from extensive green roofs E. Abdalla et al. 10.5194/hess-25-5917-2021
118. A long lead time forecast model applying an ensemble approach for managing the great Karun multi-reservoir system E. Mostaghimzadeh et al. 10.1007/s13201-023-01924-3
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124. Weekly streamflow forecasting of Rhine river based on machine learning approaches Z. Khozani et al. 10.1007/s11069-024-06962-x
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130. Survey of different data-intelligent modeling strategies for forecasting air temperature using geographic information as model predictors H. Sanikhani et al. 10.1016/j.compag.2018.07.008
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133. Predictability of Monthly Streamflow Time Series and its Relationship with Basin Characteristics: an Empirical Study Based on the MOPEX Basins R. He et al. 10.1007/s11269-020-02708-z
134. 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
135. Comparative Evaluation of Deep Learning Techniques in Streamflow Monthly Prediction of the Zarrine River Basin M. Nakhaei et al. 10.3390/w16020208
136. Medium- and Long-Term Precipitation Forecasting Method Based on Data Augmentation and Machine Learning Algorithms T. Tang et al. 10.1109/JSTARS.2022.3140442
137. A Bayesian Approach to Recreational Water Quality Model Validation and Comparison in the Presence of Measurement Error E. Potash & S. Steinschneider 10.1029/2021WR031115
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139. Extended-Range Runoff Forecasting Using a One-Way Coupled Climate–Hydrological Model: Case Studies of the Yiluo and Beijiang Rivers in China L. Liu et al. 10.3390/w11061150
140. On Predictability of Groundwater Level in Shallow Wells Using Satellite Observations M. Sahoo et al. 10.1007/s11269-017-1865-5
141. Development of a Joint Probabilistic Rainfall‐Runoff Model for High‐to‐Extreme Flow Projections Under Changing Climatic Conditions K. Li et al. 10.1029/2021WR031557
142. Identifying Trade‐Offs and Reconciling Competing Demands for Water: Integrating Agriculture Into a Robust Decision‐Making Framework J. Knox et al. 10.1002/2017EF000741
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155. Streamflow prediction based on machine learning models and rainfall estimated by remote sensing in the Brazilian Savanna and Amazon biomes transition M. de Sousa et al. 10.1007/s40808-023-01837-9
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157. Estimating annual runoff in response to forest change: A statistical method based on random forest M. Li et al. 10.1016/j.jhydrol.2020.125168
158. Comprehensive evaluation of the hydrological health evolution and its driving forces in the river-lake system W. Guo et al. 10.1016/j.ecoinf.2023.102117
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162. Agricultural Water Allocation by Integration of Hydro-Economic Modeling with Bayesian Networks and Random Forest Approaches Z. Sherafatpour et al. 10.1007/s11269-019-02240-9
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