163 citations as recorded by crossref.

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5. 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
6. Evaluation of random forests and Prophet for daily streamflow forecasting G. Papacharalampous & H. Tyralis 10.5194/adgeo-45-201-2018
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8. 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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13. A Stepwise Clustered Hydrological Model for Addressing the Temporal Autocorrelation of Daily Streamflows in Irrigated Watersheds K. Li et al. 10.1029/2021WR031065
14. Dominant process zones in a mixed fluvial–tidal delta are morphologically distinct M. Perignon et al. 10.5194/esurf-8-809-2020
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17. Monthly Rainfall-Runoff Modeling at Watershed Scale: A Comparative Study of Data-Driven and Theory-Driven Approaches W. Chang & X. Chen 10.3390/w10091116
18. Comparison of local and global approximators in multivariate chaotic forecasting of daily streamflow H. Tongal 10.1080/02626667.2020.1732983
19. The effectiveness of machine learning‐based multi‐model ensemble predictions of CMIP6 in Western Ghats of India S. Shetty et al. 10.1002/joc.8131
20. Principles and methods of scaling geospatial Earth science data Y. Ge et al. 10.1016/j.earscirev.2019.102897
21. 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
22. GTWS-MLrec: global terrestrial water storage reconstruction by machine learning from 1940 to present J. Yin et al. 10.5194/essd-15-5597-2023
23. Robust decision making in data scarce contexts: addressing data and model limitations for infrastructure planning under transient climate change J. Shortridge et al. 10.1007/s10584-016-1845-4
24. Optimum Abstraction of Groundwater for Sustaining Groundwater Level and Reducing Irrigation Cost G. Salem et al. 10.1007/s11269-017-1623-8
25. Data-driven modelling framework for streamflow prediction in a physio-climatically heterogeneous river basin P. Sharma et al. 10.1007/s00500-021-05585-9
26. Variability of Snow and Rainfall Partitioning Into Evapotranspiration and Summer Runoff Across Nine Mountainous Catchments M. Sprenger et al. 10.1029/2022GL099324
27. Estimation of daily soil temperature via data mining techniques in semi-arid climate conditions M. Sattari et al. 10.15446/esrj.v21n2.49829
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29. Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook M. Botifoll et al. 10.1039/D2NH00377E
30. Effective improvement of multi-step-ahead flood forecasting accuracy through encoder-decoder with an exogenous input structure Z. Cui et al. 10.1016/j.jhydrol.2022.127764
31. Runoff Forecasting Using Machine-Learning Methods: Case Study in the Middle Reaches of Xijiang River L. Xiao et al. 10.3389/fdata.2021.752406
32. 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
33. Development and Evaluation of the Combined Machine Learning Models for the Prediction of Dam Inflow J. Hong et al. 10.3390/w12102927
34. 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
35. Waning habitats due to climate change: the effects of changes in streamflow and temperature at the rear edge of the distribution of a cold-water fish J. Santiago et al. 10.5194/hess-21-4073-2017
36. Separating climate change and human activities' effects on flow regime with hydrological model error correction Q. Wang et al. 10.1016/j.ecolind.2023.111265
37. 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
38. A novel smoothing-based long short-term memory framework for short-to medium-range flood forecasting A. Khatun et al. 10.1080/02626667.2023.2173012
39. Detecting hydrological droughts in ungauged areas from remotely sensed hydro-meteorological variables using rule-based models J. Rhee et al. 10.1007/s11069-020-04114-5
40. Machine-learning- and deep-learning-based streamflow prediction in a hilly catchment for future scenarios using CMIP6 GCM data D. Singh et al. 10.5194/hess-27-1047-2023
41. Interpreting and modelling the daily extreme sediment events in karst mountain watersheds J. Jing et al. 10.1016/j.scitotenv.2024.171956
42. 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
43. Low-Flow (7-Day, 10-Year) Classical Statistical and Improved Machine Learning Estimation Methodologies A. DelSanto et al. 10.3390/w15152813
44. 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
45. 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
46. A multi-model integration method for monthly streamflow prediction: modified stacking ensemble strategy Y. Li et al. 10.2166/hydro.2019.066
47. A Medium and Long-Term Runoff Forecast Method Based on Massive Meteorological Data and Machine Learning Algorithms Y. Li et al. 10.3390/w13091308
48. Evaluating different machine learning methods to simulate runoff from extensive green roofs E. Abdalla et al. 10.5194/hess-25-5917-2021
49. 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
50. 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
51. Quantifying Climate and Catchment Control on Hydrological Drought in the Continental United States G. Konapala & A. Mishra 10.1029/2018WR024620
52. Spatial Analysis of Flood Susceptibility Throughout Currituck County, North Carolina J. Giovannettone et al. 10.1061/(ASCE)HE.1943-5584.0001948
53. Machine learning classification approach for formation delineation at the basin-scale D. Vikara & V. Khanna 10.1016/j.ptlrs.2021.09.004
54. Performance of Different Crop Models in Simulating Soil Temperature J. Kandasamy et al. 10.3390/s23062891
55. Competition prediction and fitness behavior based on GA-SVM algorithm and PCA model T. Li et al. 10.3233/JIFS-179202
56. A Novel Stacked Long Short-Term Memory Approach of Deep Learning for Streamflow Simulation M. Mirzaei et al. 10.3390/su132313384
57. 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
58. A machine learning approach for spatiotemporal imputation of MODIS chlorophyll-a H. Mohebzadeh et al. 10.1080/01431161.2021.1957513
59. 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
60. The Role of Satellite-Based Remote Sensing in Improving Simulated Streamflow: A Review D. Jiang & K. Wang 10.3390/w11081615
61. Machine learning models for streamflow regionalization in a tropical watershed R. Ferreira et al. 10.1016/j.jenvman.2020.111713
62. Predicting Runoff Signatures Using Regression and Hydrological Modeling Approaches Y. Zhang et al. 10.1029/2018WR023325
63. 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
64. Coupling machine learning and physical modelling for predicting runoff at catchment scale S. Zubelzu et al. 10.1016/j.jenvman.2024.120404
65. 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
66. Modeling streamflow in non-gauged watersheds with sparse data considering physiographic, dynamic climate, and anthropogenic factors using explainable soft computing techniques C. Madhushani et al. 10.1016/j.jhydrol.2024.130846
67. Application of machine learning and emerging remote sensing techniques in hydrology: A state-of-the-art review and current research trends A. Saha & S. Chandra Pal 10.1016/j.jhydrol.2024.130907
68. Long-Term Groundwater Level Prediction Model Based on Hybrid KNN-RF Technique O. Kombo et al. 10.3390/hydrology7030059
69. Predictive Modeling of Envelope Flood Extents Using Geomorphic and Climatic‐Hydrologic Catchment Characteristics R. Tavares da Costa et al. 10.1029/2019WR026453
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71. 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
72. Effects of mass balance, energy balance, and storage-discharge constraints on LSTM for streamflow prediction S. Pokharel et al. 10.1016/j.envsoft.2023.105730
73. A novel ensemble-based conceptual-data-driven approach for improved streamflow simulations A. Sikorska-Senoner & J. Quilty 10.1016/j.envsoft.2021.105094
74. 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
75. Comparative Evaluation of Deep Learning Techniques in Streamflow Monthly Prediction of the Zarrine River Basin M. Nakhaei et al. 10.3390/w16020208
76. 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
77. Medium- and Long-Term Precipitation Forecasting Method Based on Data Augmentation and Machine Learning Algorithms T. Tang et al. 10.1109/JSTARS.2022.3140442
78. Streamflow simulation using conceptual and neural network models in the Hemavathi sub-watershed, India N. Reddy et al. 10.1016/j.geogeo.2022.100153
79. On constructing limits-of-acceptability in watershed hydrology using decision trees A. Gupta et al. 10.1016/j.advwatres.2023.104486
80. A hybrid bayesian vine model for water level prediction Z. Liu et al. 10.1016/j.envsoft.2021.105075
81. Reliability Assessment of Machine Learning Models in Hydrological Predictions Through Metamorphic Testing Y. Yang & T. Chui 10.1029/2020WR029471
82. A Bayesian Approach to Recreational Water Quality Model Validation and Comparison in the Presence of Measurement Error E. Potash & S. Steinschneider 10.1029/2021WR031115
83. Long-Term Streamflow Forecasting Based on Relevance Vector Machine Model Y. Liu et al. 10.3390/w9010009
84. 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
85. Modelling the Effect and Variability of Integrated Weed Management of Phalaris minor in Rice-Wheat Cropping Systems in Northern India C. Liu et al. 10.3390/agronomy11112331
86. 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
87. On Predictability of Groundwater Level in Shallow Wells Using Satellite Observations M. Sahoo et al. 10.1007/s11269-017-1865-5
88. Improving predictions of hydrological low-flow indices in ungaged basins using machine learning S. Worland et al. 10.1016/j.envsoft.2017.12.021
89. 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
90. 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
91. 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
92. Identifying Trade‐Offs and Reconciling Competing Demands for Water: Integrating Agriculture Into a Robust Decision‐Making Framework J. Knox et al. 10.1002/2017EF000741
93. Forecasting salinity time series using RF and ELM approaches coupled with decomposition techniques J. Hu et al. 10.1007/s00477-019-01691-1
94. Uncertainty quantification of machine learning models to improve streamflow prediction under changing climate and environmental conditions S. Liu et al. 10.3389/frwa.2023.1150126
95. Calibrating a Hydrological Model in an Ungauged Mountain Basin with the Budyko Framework Z. Yu et al. 10.3390/w14193112
96. Analysis of regression prediction model of competitive sports based on SVM and artificial intelligence J. Wang et al. 10.3233/JIFS-189061
97. 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
98. 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
99. Prediction of multi-sectoral longitudinal water withdrawals using hierarchical machine learning models J. Shortridge 10.2166/hydro.2023.110
100. A Hybrid Model for Runoff Prediction Using Variational Mode Decomposition and Artificial Neural Network . Muhammad Sibtain et al. 10.1134/S0097807821050171
101. Streamflow forecasting in Tocantins river basins using machine learning V. Duarte et al. 10.2166/ws.2022.155
102. Predicting national-scale tile drainage discharge in Denmark using machine learning algorithms S. Motarjemi et al. 10.1016/j.ejrh.2021.100839
103. Predicting Inflow Rate of the Soyang River Dam Using Deep Learning Techniques S. Lee & J. Kim 10.3390/w13172447
104. Machine Learning Models for Mapping Potentially Toxic Elements and Assessing Human Health Risk in Soils Affected by Mining Activity E. Paes et al. 10.2139/ssrn.4176123
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113. 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
114. Robust decision outcomes with induced correlations in climatic and economic parameters J. Reis & J. Shortridge 10.1007/s11027-021-09970-5
115. Towards an efficient streamflow forecasting method for event-scales in Ca River basin, Vietnam X. Le et al. 10.1016/j.ejrh.2023.101328
116. 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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118. 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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125. 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
126. 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
127. Mapping the temporary and perennial character of whole river networks A. González‐Ferreras & J. Barquín 10.1002/2017WR020390
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131. Hourly River Flow Forecasting: Application of Emotional Neural Network Versus Multiple Machine Learning Paradigms Z. Yaseen et al. 10.1007/s11269-020-02484-w
132. Validity of Machine Learning in Assessing Large Texts Through Sustainability Indicators J. García-Esparza et al. 10.1007/s11205-023-03075-z
133. Boreholes Data Analysis Architecture Based on Clustering and Prediction Models for Enhancing Underground Safety Verification N. Iqbal et al. 10.1109/ACCESS.2021.3083175
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135. Distributed long-term hourly streamflow predictions using deep learning – A case study for State of Iowa Z. Xiang & I. Demir 10.1016/j.envsoft.2020.104761
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158. 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
159. Estimating the Impacts of Ungauged Reservoirs Using Publicly Available Streamflow Simulations and Satellite Remote Sensing N. Nguyen et al. 10.3390/rs15184563
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162. Development of regional flood frequency analysis techniques using generalized additive models for Australia A. Rahman et al. 10.1007/s00477-017-1384-1
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