144 citations as recorded by crossref.

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34. A Spatially Enhanced Data‐Driven Multimodel to Improve Semiseasonal Groundwater Forecasts in the High Plains Aquifer, USA A. Amaranto et al. 10.1029/2018WR024301
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41. 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
42. 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
43. 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
44. Predicting standard penetration test N-value from cone penetration test data using artificial neural networks B. Tarawneh 10.1016/j.gsf.2016.02.003
45. 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
46. Machine learning based identification of dominant controls on runoff dynamics H. Oppel & A. Schumann 10.1002/hyp.13740
47. 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
48. A spatio-temporal hybrid neural network-Kriging model for groundwater level simulation E. Tapoglou et al. 10.1016/j.jhydrol.2014.10.040
49. Evaluation of short-term streamflow prediction methods in Urban river basins X. Huang et al. 10.1016/j.pce.2021.103027
50. Streamflow prediction using support vector regression machine learning model for Tehri Dam B. Sharma & N. Goel 10.1007/s13201-024-02135-0
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52. Analyzing drought characteristics using copula-based genetic algorithm method H. Kiafar et al. 10.1007/s12517-020-05703-1
53. 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
54. Incorporating basic hydrological concepts into genetic programming for rainfall-runoff forecasting V. Havlíček et al. 10.1007/s00607-013-0298-0
55. 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
56. 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
57. 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
58. Model structure exploration for index flood regionalization D. Han & W. Jaafar 10.1002/hyp.9436
59. 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
60. 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
61. 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
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63. Semi-seasonal groundwater forecast using multiple data-driven models in an irrigated cropland A. Amaranto et al. 10.2166/hydro.2018.002
64. Monthly Rainfall-Runoff Modeling at Watershed Scale: A Comparative Study of Data-Driven and Theory-Driven Approaches W. Chang & X. Chen 10.3390/w10091116
65. 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
66. State-of-the-art review of some artificial intelligence applications in pile foundations M. Shahin 10.1016/j.gsf.2014.10.002
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68. 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
69. 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
70. 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
71. MONTHLY STREAM FLOWS ESTIMATION IN THE KARASU RIVER OF EUPHRATES BASIN WITH ARTIFICIAL NEURAL NETWORKS APPROACH O. KATİPOĞLU 10.21923/jesd.982868
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74. Comparison of methods used for quantifying prediction interval in artificial neural network hydrologic models K. Kasiviswanathan & K. Sudheer 10.1007/s40808-016-0079-9
75. 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
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77. Quantifying the Uncertainties in Data-Driven Models for Reservoir Inflow Prediction X. Zhang et al. 10.1007/s11269-020-02514-7
78. Estimation of ANN prediction bounds for the suspended sediment load modeling V. Nourani et al. 10.1088/1755-1315/491/1/012001
79. Evaluation of remote sensing-based evapotranspiration products at low-latitude eddy covariance sites D. Salazar-Martínez et al. 10.1016/j.jhydrol.2022.127786
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81. Support vector regression optimized by meta-heuristic algorithms for daily streamflow prediction A. Malik et al. 10.1007/s00477-020-01874-1
82. Integrating Artificial Neural Networks into the VIC Model for Rainfall-Runoff Modeling C. Meng et al. 10.3390/w8090407
83. 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
84. Deep learning in hydrology and water resources disciplines: concepts, methods, applications, and research directions K. Tripathy & A. Mishra 10.1016/j.jhydrol.2023.130458
85. Runoff prediction of lower Yellow River based on CEEMDAN–LSSVM–GM(1,1) model S. Guo et al. 10.1038/s41598-023-28662-5
86. Moment-based metrics for global sensitivity analysis of hydrological systems A. Dell'Oca et al. 10.5194/hess-21-6219-2017
87. 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
88. A Stepwise Clustered Hydrological Model for Addressing the Temporal Autocorrelation of Daily Streamflows in Irrigated Watersheds K. Li et al. 10.1029/2021WR031065
89. Artificial neural network model for ozone concentration estimation and Monte Carlo analysis M. Gao et al. 10.1016/j.atmosenv.2018.03.027
90. 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
91. 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
92. Optimising Fuzzy Neural Network Architecture for Dissolved Oxygen Prediction and Risk Analysis U. Khan & C. Valeo 10.3390/w9060381
93. Quantifying Climate and Catchment Control on Hydrological Drought in the Continental United States G. Konapala & A. Mishra 10.1029/2018WR024620
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99. 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
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106. Quantifying the Performances of the Semi-Distributed Hydrologic Model in Parallel Computing—A Case Study J. Kim & J. Ryu 10.3390/w11040823
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108. Supporting M5 model trees with sensitivity information derived from conceptual hydrological models C. Massmann 10.2166/hydro.2015.111
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