122 citations as recorded by crossref.

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45. 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
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49. 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
50. 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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52. Semi-seasonal groundwater forecast using multiple data-driven models in an irrigated cropland A. Amaranto et al. 10.2166/hydro.2018.002
53. Monthly Rainfall-Runoff Modeling at Watershed Scale: A Comparative Study of Data-Driven and Theory-Driven Approaches W. Chang & X. Chen 10.3390/w10091116
54. 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
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57. 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
58. 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
59. 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
60. Comparison of methods used for quantifying prediction interval in artificial neural network hydrologic models K. Kasiviswanathan & K. Sudheer 10.1007/s40808-016-0079-9
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63. Quantifying the Uncertainties in Data-Driven Models for Reservoir Inflow Prediction X. Zhang et al. 10.1007/s11269-020-02514-7
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65. Modular conceptual modelling approach and software for river hydraulic simulations V. Wolfs et al. 10.1016/j.envsoft.2015.05.010
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68. 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
69. Moment-based metrics for global sensitivity analysis of hydrological systems A. Dell'Oca et al. 10.5194/hess-21-6219-2017
70. 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
71. A Stepwise Clustered Hydrological Model for Addressing the Temporal Autocorrelation of Daily Streamflows in Irrigated Watersheds K. Li et al. 10.1029/2021WR031065
72. Artificial neural network model for ozone concentration estimation and Monte Carlo analysis M. Gao et al. 10.1016/j.atmosenv.2018.03.027
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74. Optimising Fuzzy Neural Network Architecture for Dissolved Oxygen Prediction and Risk Analysis U. Khan & C. Valeo 10.3390/w9060381
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81. 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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83. 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
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86. River ice breakup timing prediction through stacking multi-type model trees W. Sun 10.1016/j.scitotenv.2018.07.001
87. 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
88. Quantifying the Performances of the Semi-Distributed Hydrologic Model in Parallel Computing—A Case Study J. Kim & J. Ryu 10.3390/w11040823
89. 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
90. Supporting M5 model trees with sensitivity information derived from conceptual hydrological models C. Massmann 10.2166/hydro.2015.111
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