148 citations as recorded by crossref.

1. Enhancing robustness of monthly streamflow forecasting model using gated recurrent unit based on improved grey wolf optimizer X. Zhao et al. 10.1016/j.jhydrol.2021.126607
2. Impact of calibrating a low-cost capacitance-based soil moisture sensor on AquaCrop model performance S. Adla et al. 10.1016/j.jenvman.2024.120248
3. Application of Artificial Neural Network and Information Entropy Theory to Assess Rainfall Station Distribution: A Case Study from Colombia A. Garrido-Arévalo et al. 10.3390/w12071973
4. Dissolved oxygen prediction using a possibility theory based fuzzy neural network U. Khan & C. Valeo 10.5194/hess-20-2267-2016
5. Successive-station monthly streamflow prediction using neuro-wavelet technique A. Danandeh Mehr et al. 10.1007/s12145-013-0141-3
6. Investigating capabilities of machine learning techniques in forecasting stream flow S. Kabir et al. 10.1680/jwama.19.00001
7. Identifying Regional Models for Flow Duration Curves with Evolutionary Polynomial Regression: Application for Intermittent Streams V. Costa et al. 10.1061/(ASCE)HE.1943-5584.0001873
8. River flood prediction using fuzzy neural networks: an investigation on automated network architecture U. Khan et al. 10.2166/wst.2018.107
9. Sensitivity analysis of data-driven groundwater forecasts to hydroclimatic controls in irrigated croplands A. Amaranto et al. 10.1016/j.jhydrol.2020.124957
10. Regionalization of runoff models derived by genetic programming M. Heřmanovský et al. 10.1016/j.jhydrol.2017.02.018
11. On the Automation of Flood Event Separation From Continuous Time Series H. Oppel & B. Mewes 10.3389/frwa.2020.00018
12. Application of data-driven methods to predict the sodium adsorption rate (SAR) in different climates in Iran E. Rahnama et al. 10.1007/s12517-020-06146-4
13. Linear genetic programming application for successive-station monthly streamflow prediction A. Danandeh Mehr et al. 10.1016/j.cageo.2014.04.015
14. Linking plant and soil indices for water stress management in black gram A. Khorsand et al. 10.1038/s41598-020-79516-3
15. Constructing prediction interval for artificial neural network rainfall runoff models based on ensemble simulations K. Kasiviswanathan et al. 10.1016/j.jhydrol.2013.06.043
16. Meteo and Hydrodynamic Measurements to Detect Physical Processes in Confined Shallow Seas F. De Serio & M. Mossa 10.3390/s18010280
17. Uncertainties in the application of artificial neural networks in ocean engineering N. Portillo Juan et al. 10.1016/j.oceaneng.2023.115193
18. Prediction of Water Retention of Soils from the Humid Tropics by the Nonparametric k‐Nearest Neighbor Approach Y. Botula et al. 10.2136/vzj2012.0123
19. Review of hydrological modelling in the Australian Alps: from rainfall-runoff to physically based models N. Harvey et al. 10.1080/13241583.2024.2343453
20. Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 2: Application A. Elshorbagy et al. 10.5194/hess-14-1943-2010
21. Hydrological Early Warning System Based on a Deep Learning Runoff Model Coupled with a Meteorological Forecast A. de la Fuente et al. 10.3390/w11091808
22. Streamflow estimation using satellite-retrieved water fluxes and machine learning technique over monsoon-dominated catchments of India D. Dayal et al. 10.1080/02626667.2021.1889557
23. Product-Units neural networks for catchment runoff forecasting A. Piotrowski & J. Napiorkowski 10.1016/j.advwatres.2012.05.016
24. An application of artificial neural networks for modeling formaldehyde emission based on process parameters in particleboard manufacturing process İ. Akyüz et al. 10.1007/s10098-017-1342-0
25. Machine learning methods for empirical streamflow simulation: a comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds J. Shortridge et al. 10.5194/hess-20-2611-2016
26. Hydrologically Informed Machine Learning for Rainfall‐Runoff Modeling: A Genetic Programming‐Based Toolkit for Automatic Model Induction J. Chadalawada et al. 10.1029/2019WR026933
27. Evaluating the performance of random forest for large-scale flood discharge simulation L. Schoppa et al. 10.1016/j.jhydrol.2020.125531
28. Modeling of groundwater level fluctuations using dendrochronology in alluvial aquifers V. Gholami et al. 10.1016/j.jhydrol.2015.09.028
29. Multi-time-step ahead daily and hourly intermittent reservoir inflow prediction by artificial intelligent techniques using lumped and distributed data V. Jothiprakash & R. Magar 10.1016/j.jhydrol.2012.04.045
30. Quantile-Based Downscaling of Precipitation Using Genetic Programming: Application to IDF Curves in Saskatoon E. Hassanzadeh et al. 10.1061/(ASCE)HE.1943-5584.0000854
31. Interactions among climate, topography, soil structure and rangeland aboveground net primary production S. Mohammadi Moghaddam et al. 10.1071/RJ21013
32. Analysis of data characterizing tide and current fluxes in coastal basins E. Armenio et al. 10.5194/hess-21-3441-2017
33. Assessing the predictive capability of randomized tree-based ensembles in streamflow modelling S. Galelli & A. Castelletti 10.5194/hess-17-2669-2013
34. Improving probabilistic flood forecasting through a data assimilation scheme based on genetic programming L. Mediero et al. 10.5194/nhess-12-3719-2012
35. A Spatially Enhanced Data‐Driven Multimodel to Improve Semiseasonal Groundwater Forecasts in the High Plains Aquifer, USA A. Amaranto et al. 10.1029/2018WR024301
36. Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting R. Abrahart et al. 10.1177/0309133312444943
37. Soil Water Dynamic Modeling Using the Physical and Support Vector Machine Methods K. Lamorski et al. 10.2136/vzj2013.05.0085
38. Geospatial modeling using hybrid machine learning approach for flood susceptibility B. Mishra et al. 10.1007/s12145-022-00872-x
39. A Deep Learning Framework for Flash-Flood-Runoff Prediction: Integrating CNN-RNN with Neural Ordinary Differential Equations (ODEs) K. Alkaabi et al. 10.3390/w17091283
40. Detecting flood prone areas in Harris County: a GIS based analysis F. Mukherjee & D. Singh 10.1007/s10708-019-09984-2
41. A hybrid data-driven approach to analyze the drivers of lake level dynamics M. Somogyvári et al. 10.5194/hess-28-4331-2024
42. Enhancing streamflow simulation using hybridized machine learning models in a semi-arid basin of the Chinese loess Plateau Q. Yu et al. 10.1016/j.jhydrol.2023.129115
43. Choice of rainfall inputs for event-based rainfall-runoff modeling in a catchment with multiple rainfall stations using data-driven techniques T. Chang et al. 10.1016/j.jhydrol.2016.12.024
44. Inferring catchment flow path responses using a data-driven model: an exploratory study based on a generalized additive model D. Kundu et al. 10.1080/02626667.2017.1357887
45. Methods used for handling and quantifying model uncertainty of artificial neural network models for air pollution forecasting S. Cabaneros & B. Hughes 10.1016/j.envsoft.2022.105529
46. Predicting standard penetration test N-value from cone penetration test data using artificial neural networks B. Tarawneh 10.1016/j.gsf.2016.02.003
47. Reviews and Syntheses: Promoting the Advancement of Hillslope Hydrology and Stability in Taiwan from the Perspective of Critical Zone Science Y. Yang et al. 10.3390/w15061234
48. Machine learning based identification of dominant controls on runoff dynamics H. Oppel & A. Schumann 10.1002/hyp.13740
49. Multi-objective optimization of typhoon inundation forecast models with cross-site structures for a water-level gauging network by integrating ARMAX with a genetic algorithm H. Ouyang 10.5194/nhess-16-1897-2016
50. A spatio-temporal hybrid neural network-Kriging model for groundwater level simulation E. Tapoglou et al. 10.1016/j.jhydrol.2014.10.040
51. Evaluation of short-term streamflow prediction methods in Urban river basins X. Huang et al. 10.1016/j.pce.2021.103027
52. Streamflow prediction using support vector regression machine learning model for Tehri Dam B. Sharma & N. Goel 10.1007/s13201-024-02135-0
53. Skill of large-scale seasonal drought impact forecasts S. Sutanto et al. 10.5194/nhess-20-1595-2020
54. Analyzing drought characteristics using copula-based genetic algorithm method H. Kiafar et al. 10.1007/s12517-020-05703-1
55. Multi-time-step ahead daily global solar radiation forecasting: performance evaluation of wavelet-based artificial neural network model S. Sharifi et al. 10.1007/s00703-022-00882-w
56. Incorporating basic hydrological concepts into genetic programming for rainfall-runoff forecasting V. Havlíček et al. 10.1007/s00607-013-0298-0
57. How Well Can Machine Learning Models Perform without Hydrologists? Application of Rational Feature Selection to Improve Hydrological Forecasting V. Moreido et al. 10.3390/w13121696
58. Data-driven modelling approaches for socio-hydrology: opportunities and challenges within the Panta Rhei Science Plan N. Mount et al. 10.1080/02626667.2016.1159683
59. Elucidating the relationship between gaseous O2 and redox potential in a soil aquifer treatment system using data driven approaches and an oxygen diffusion model T. Turkeltaub et al. 10.1016/j.jhydrol.2023.129168
60. Model structure exploration for index flood regionalization D. Han & W. Jaafar 10.1002/hyp.9436
61. A hybrid model of self organizing maps and least square support vector machine for river flow forecasting S. Ismail et al. 10.5194/hess-16-4417-2012
62. Data-driven techniques for modelling the gross primary production of the páramo vegetation using climate data: Application in the Ecuadorian Andean region V. Minaya et al. 10.1016/j.ecoinf.2016.12.002
63. Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan M. Tsai et al. 10.1002/hyp.9559
64. Pedotransfer functions to predict water retention for soils of the humid tropics: a review Y. Botula et al. 10.1590/S0100-06832014000300001
65. Semi-seasonal groundwater forecast using multiple data-driven models in an irrigated cropland A. Amaranto et al. 10.2166/hydro.2018.002
66. Monthly Rainfall-Runoff Modeling at Watershed Scale: A Comparative Study of Data-Driven and Theory-Driven Approaches W. Chang & X. Chen 10.3390/w10091116
67. Combined TBATS and SVM model of minimum and maximum air temperatures applied to wheat yield prediction at different locations in Europe M. Gos et al. 10.1016/j.agrformet.2019.107827
68. State-of-the-art review of some artificial intelligence applications in pile foundations M. Shahin 10.1016/j.gsf.2014.10.002
69. An evaluation framework for input variable selection algorithms for environmental data-driven models S. Galelli et al. 10.1016/j.envsoft.2014.08.015
70. Influence of lag time on event-based rainfall–runoff modeling using the data driven approach A. Talei & L. Chua 10.1016/j.jhydrol.2012.03.027
71. Observed and projected trends in spring flood discharges for the Upper Harricana River, eastern boreal Canada A. Nolin et al. 10.1016/j.ejrh.2023.101462
72. On the importance of training methods and ensemble aggregation for runoff prediction by means of artificial neural networks A. Piotrowski et al. 10.1080/02626667.2015.1085650
73. MONTHLY STREAM FLOWS ESTIMATION IN THE KARASU RIVER OF EUPHRATES BASIN WITH ARTIFICIAL NEURAL NETWORKS APPROACH O. KATİPOĞLU 10.21923/jesd.982868
74. Performance of machine learning methods for modeling reservoir management based on irregular daily data sets: a case study of Zit Emba dam B. Lefoula et al. 10.1007/s12145-023-01160-y
75. Worldwide evaluation of mean and extreme runoff from six global-scale hydrological models that account for human impacts J. Zaherpour et al. 10.1088/1748-9326/aac547
76. Comparison of methods used for quantifying prediction interval in artificial neural network hydrologic models K. Kasiviswanathan & K. Sudheer 10.1007/s40808-016-0079-9
77. The skill of seasonal ensemble low-flow forecasts in the Moselle River for three different hydrological models M. Demirel et al. 10.5194/hess-19-275-2015
78. A review of artificial intelligence applications in shallow foundations M. Shahin 10.1179/1939787914Y.0000000058
79. Quantifying the Uncertainties in Data-Driven Models for Reservoir Inflow Prediction X. Zhang et al. 10.1007/s11269-020-02514-7
80. Estimation of ANN prediction bounds for the suspended sediment load modeling V. Nourani et al. 10.1088/1755-1315/491/1/012001
81. Evaluation of remote sensing-based evapotranspiration products at low-latitude eddy covariance sites D. Salazar-Martínez et al. 10.1016/j.jhydrol.2022.127786
82. Modular conceptual modelling approach and software for river hydraulic simulations V. Wolfs et al. 10.1016/j.envsoft.2015.05.010
83. Support vector regression optimized by meta-heuristic algorithms for daily streamflow prediction A. Malik et al. 10.1007/s00477-020-01874-1
84. Integrating Artificial Neural Networks into the VIC Model for Rainfall-Runoff Modeling C. Meng et al. 10.3390/w8090407
85. A Hybrid Method for the Run-Of-The-River Hydroelectric Power Plant Energy Forecast: HYPE Hydrological Model and Neural Network E. Ogliari et al. 10.3390/forecast2040022
86. Deep learning in hydrology and water resources disciplines: concepts, methods, applications, and research directions K. Tripathy & A. Mishra 10.1016/j.jhydrol.2023.130458
87. Runoff prediction of lower Yellow River based on CEEMDAN–LSSVM–GM(1,1) model S. Guo et al. 10.1038/s41598-023-28662-5
88. Moment-based metrics for global sensitivity analysis of hydrological systems A. Dell'Oca et al. 10.5194/hess-21-6219-2017
89. Assessing the effects of climate change on monthly precipitation: Proposing of a downscaling strategy through a case study in Turkey U. Okkan 10.1007/s12205-014-0052-y
90. A Stepwise Clustered Hydrological Model for Addressing the Temporal Autocorrelation of Daily Streamflows in Irrigated Watersheds K. Li et al. 10.1029/2021WR031065
91. Artificial neural network model for ozone concentration estimation and Monte Carlo analysis M. Gao et al. 10.1016/j.atmosenv.2018.03.027
92. Construction and evaluation of pedotransfer functions for saturated hydraulic conductivity in the granite red soil regions of southern China L. He et al. 10.1016/j.ejrh.2023.101539
93. Investigating most appropriate method for estimating suspended sediment load based on error criterias in arid and semi-arid areas (case study of Kardeh Dam watershed stations) H. Mousazadeh et al. 10.1007/s12517-021-08414-3
94. Optimising Fuzzy Neural Network Architecture for Dissolved Oxygen Prediction and Risk Analysis U. Khan & C. Valeo 10.3390/w9060381
95. Quantifying Climate and Catchment Control on Hydrological Drought in the Continental United States G. Konapala & A. Mishra 10.1029/2018WR024620
96. Self-Learning Cellular Automata for Forecasting Precipitation from Radar Images H. Li et al. 10.1061/(ASCE)HE.1943-5584.0000646
97. Reconstructing input for artificial neural networks based on embedding theory and mutual information to simulate soil pore water salinity in tidal floodplain F. Zheng et al. 10.1002/2014WR016875
98. Ideal point error for model assessment in data-driven river flow forecasting C. Dawson et al. 10.5194/hess-16-3049-2012
99. Hybrid modelling approach to prairie hydrology: fusing data-driven and process-based hydrological models B. Mekonnen et al. 10.1080/02626667.2014.935778
100. An ensemble simulation approach for artificial neural network: An example from chlorophyll a simulation in Lake Poyang, China J. Huang & J. Gao 10.1016/j.ecoinf.2016.11.012
101. Displacement Prediction of a Complex Landslide in the Three Gorges Reservoir Area (China) Using a Hybrid Computational Intelligence Approach J. Ma et al. 10.1155/2020/2624547
102. Water demand modelling using evolutionary computation techniques: integrating water equity and justice for realization of the sustainable development goals O. Oyebode et al. 10.1016/j.heliyon.2019.e02796
103. An Overview of Evapotranspiration Estimation Models Utilizing Artificial Intelligence M. Taheri et al. 10.3390/w17091384
104. Regional Modeling of Long-Term and Annual Flow Duration Curves: Reliability for Information Transfer with Evolutionary Polynomial Regression V. Costa & W. Fernandes 10.1061/(ASCE)HE.1943-5584.0002051
105. Neural network modeling of hydrological systems: A review of implementation techniques O. Oyebode & D. Stretch 10.1111/nrm.12189
106. Quantification of the predictive uncertainty of artificial neural network based river flow forecast models K. Kasiviswanathan & K. Sudheer 10.1007/s00477-012-0600-2
107. River ice breakup timing prediction through stacking multi-type model trees W. Sun 10.1016/j.scitotenv.2018.07.001
108. Comparison of the use of a physical-based model with data assimilation and machine learning methods for simulating soil water dynamics P. Li et al. 10.1016/j.jhydrol.2020.124692
109. Quantifying the Performances of the Semi-Distributed Hydrologic Model in Parallel Computing—A Case Study J. Kim & J. Ryu 10.3390/w11040823
110. Mountain-river runoff components and their role in the seasonal development of desert-oases in northwest China M. Matin & C. Bourque 10.1016/j.jaridenv.2015.05.011
111. Supporting M5 model trees with sensitivity information derived from conceptual hydrological models C. Massmann 10.2166/hydro.2015.111
112. A Soil Moisture Prediction Model, Based on Depth and Water Balance Equation: A Case Study of the Xilingol League Grassland R. Fu et al. 10.3390/ijerph20021374
113. Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling W. Wu et al. 10.1016/j.envsoft.2013.12.016
114. Improving predictions of hydrological low-flow indices in ungaged basins using machine learning S. Worland et al. 10.1016/j.envsoft.2017.12.021
115. Prediction and Inference of Flow Duration Curves Using Multioutput Neural Networks S. Worland et al. 10.1029/2018WR024463
116. Performance Assessment of the Linear, Nonlinear and Nonparametric Data Driven Models in River Flow Forecasting A. Ahani et al. 10.1007/s11269-017-1792-5
117. A ranking system for comparing models' performance combining multiple statistical criteria and scenarios: The case of reference evapotranspiration models V. Aschonitis et al. 10.1016/j.envsoft.2019.01.005
118. Machine learning models for streamflow regionalization in a tropical watershed R. Ferreira et al. 10.1016/j.jenvman.2020.111713
119. Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS M. Tehrany et al. 10.1016/j.jhydrol.2014.03.008
120. Methods used for quantifying the prediction uncertainty of artificial neural network based hydrologic models K. Kasiviswanathan & K. Sudheer 10.1007/s00477-016-1369-5
121. Runoff forecasting using a Takagi–Sugeno neuro-fuzzy model with online learning A. Talei et al. 10.1016/j.jhydrol.2013.02.022
122. Application of rule based methods to predicting storm surge S. Royston et al. 10.1016/j.csr.2012.02.018
123. Prediction Interval Estimation Methods for Artificial Neural Network (ANN)-Based Modeling of the Hydro-Climatic Processes, a Review V. Nourani et al. 10.3390/su13041633
124. Comparison of Interpolation, Statistical, and Data-Driven Methods for Imputation of Missing Values in a Distributed Soil Moisture Dataset K. Kornelsen & P. Coulibaly 10.1061/(ASCE)HE.1943-5584.0000767
125. Correlation approach in predictor selection for groundwater level forecasting in areas threatened by water deficits J. Kajewska-Szkudlarek et al. 10.2166/hydro.2021.059
126. Urban Water Demand Forecasting: A Comparative Evaluation of Conventional and Soft Computing Techniques O. Oyebode & D. Ighravwe 10.3390/resources8030156
127. Precipitation concentration index management by adaptive neuro-fuzzy methodology D. Petković et al. 10.1007/s10584-017-1907-2
128. Frequency based imputation of precipitation F. Dikbas 10.1007/s00477-016-1356-x
129. Recent advance in earth observation big data for hydrology L. Chen & L. Wang 10.1080/20964471.2018.1435072
130. Approaches for Optimizing the Performance of Adaptive Neuro-Fuzzy Inference System and Least-Squares Support Vector Machine in Precipitation Modeling A. Azad et al. 10.1061/(ASCE)HE.1943-5584.0002069
131. Estimation of Moisture Content in Thickened Tailings Dams: Machine Learning Techniques Applied to Remote Sensing Images G. Arancibia et al. 10.1109/ACCESS.2021.3053767
132. Assessing the Propagation from Meteorological to Hydrological Drought in the São Francisco River Catchment with Standardized Indexes: Exploratory Analysis, Influential Factors, and Forecasting Strategies V. Costa et al. 10.1061/(ASCE)WR.1943-5452.0001464
133. Meteorological Drought Forecasting Based on Climate Signals Using Artificial Neural Network – A Case Study in Khanhhoa Province Vietnam M. Le et al. 10.1016/j.proeng.2016.07.528
134. Data-based groundwater quality estimation and uncertainty analysis for irrigation agriculture H. Yu et al. 10.1016/j.agwat.2021.107423
135. A Model Tree Generator (MTG) Framework for Simulating Hydrologic Systems: Application to Reservoir Routing M. Rahnamay Naeini et al. 10.3390/w12092373
136. Nonlinear regression in environmental sciences using extreme learning machines: A comparative evaluation A. Lima et al. 10.1016/j.envsoft.2015.08.002
137. Floods modeling and analysis for Dubai using HEC-HMS model and remote sensing using GIS I. Khan et al. 10.1038/s41598-024-74736-3
138. Bootstrap rank‐ordered conditional mutual information (broCMI): A nonlinear input variable selection method for water resources modeling J. Quilty et al. 10.1002/2015WR016959
139. Research on Water Resource Modeling Based on Machine Learning Technologies Z. Liu et al. 10.3390/w16030472
140. Evaluation of random forests and Prophet for daily streamflow forecasting G. Papacharalampous & H. Tyralis 10.5194/adgeo-45-201-2018
141. Developed strategies of artificial intelligence in the prediction flow river flood using evolutionary optimized algorithms of ANN R. Ikram et al. 10.1007/s10668-024-05795-9
142. Streamflow forecasting in Tocantins river basins using machine learning V. Duarte et al. 10.2166/ws.2022.155
143. A nonlinear spatio-temporal lumping of radar rainfall for modeling multi-step-ahead inflow forecasts by data-driven techniques F. Chang & M. Tsai 10.1016/j.jhydrol.2016.01.056
144. Predicting the geometry of regime rivers using M5 model tree, multivariate adaptive regression splines and least square support vector regression methods S. Shaghaghi et al. 10.1080/15715124.2018.1546731
145. Determination of Input for Artificial Neural Networks for Flood Forecasting Using the Copula Entropy Method L. Chen et al. 10.1061/(ASCE)HE.1943-5584.0000932
146. Advancing flood warning procedures in ungauged basins with machine learning Z. Rasheed et al. 10.1016/j.jhydrol.2022.127736
147. Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques A. Lohani et al. 10.1016/j.jhydrol.2012.03.031
148. Time Series Forecasting Using Wavelet-Least Squares Support Vector Machines and Wavelet Regression Models for Monthly Stream Flow Data S. Pandhiani & A. Shabri 10.4236/ojs.2013.33021