107 citations as recorded by crossref.

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15. Modeling of groundwater level fluctuations using dendrochronology in alluvial aquifers V. Gholami et al. 10.1016/j.jhydrol.2015.09.028
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17. Assessing the predictive capability of randomized tree-based ensembles in streamflow modelling S. Galelli & A. Castelletti 10.5194/hess-17-2669-2013
18. The Impact of Training Data Sequence on the Performance of Neuro-Fuzzy Rainfall-Runoff Models with Online Learning T. Chang et al. 10.3390/w11010052
19. Improving probabilistic flood forecasting through a data assimilation scheme based on genetic programming L. Mediero et al. 10.5194/nhess-12-3719-2012
20. A Spatially Enhanced Data‐Driven Multimodel to Improve Semiseasonal Groundwater Forecasts in the High Plains Aquifer, USA A. Amaranto et al. 10.1029/2018WR024301
21. Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting R. Abrahart et al. 10.1177/0309133312444943
22. Entropy Ensemble Filter: A Modified Bootstrap Aggregating (Bagging) Procedure to Improve Efficiency in Ensemble Model Simulation H. Foroozand & S. Weijs 10.3390/e19100520
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27. A combined deep CNN-RNN network for rainfall-runoff modelling in Bardha Watershed, India P. Shekar et al. 10.1016/j.aiig.2024.100073
28. A spatio-temporal hybrid neural network-Kriging model for groundwater level simulation E. Tapoglou et al. 10.1016/j.jhydrol.2014.10.040
29. B-AMA: A Python-coded protocol to enhance the application of data-driven models in hydrology A. Amaranto & M. Mazzoleni 10.1016/j.envsoft.2022.105609
30. Reconstruction of the pan evaporation based on meteorological factors with machine learning method over China H. Wang et al. 10.1016/j.agwat.2023.108416
31. 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
32. Impact of temperature changes on groundwater levels and irrigation costs in a groundwater-dependent agricultural region in Northwest Bangladesh G. Salem et al. 10.3178/hrl.11.85
33. 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
34. Incorporating basic hydrological concepts into genetic programming for rainfall-runoff forecasting V. Havlíček et al. 10.1007/s00607-013-0298-0
35. A comparison of methods to avoid overfitting in neural networks training in the case of catchment runoff modelling A. Piotrowski & J. Napiorkowski 10.1016/j.jhydrol.2012.10.019
36. 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
37. Semi-seasonal groundwater forecast using multiple data-driven models in an irrigated cropland A. Amaranto et al. 10.2166/hydro.2018.002
38. Pedotransfer functions to predict water retention for soils of the humid tropics: a review Y. Botula et al. 10.1590/S0100-06832014000300001
39. 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
40. Rainfall-Runoff modelling using SWAT and eight artificial intelligence models in the Murredu Watershed, India P. Shekar et al. 10.1007/s10661-023-11649-0
41. A Probabilistic Runoff Prediction Model Based on Improved Long Short-Term Memory and Interval Correction S. Zhu et al. 10.1061/JHYEFF.HEENG-6091
42. Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models T. Lees et al. 10.5194/hess-25-5517-2021
43. Monthly Rainfall-Runoff Modeling at Watershed Scale: A Comparative Study of Data-Driven and Theory-Driven Approaches W. Chang & X. Chen 10.3390/w10091116
44. Physically based vs. data-driven models for streamflow and reservoir volume prediction at a data-scarce semi-arid basin G. Özdoğan-Sarıkoç & F. Dadaser-Celik 10.1007/s11356-024-33732-w
45. 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
46. 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
47. Comparing various artificial neural network types for water temperature prediction in rivers A. Piotrowski et al. 10.1016/j.jhydrol.2015.07.044
48. 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
49. Comparison of methods used for quantifying prediction interval in artificial neural network hydrologic models K. Kasiviswanathan & K. Sudheer 10.1007/s40808-016-0079-9
50. Optimizing neural networks for river flow forecasting – Evolutionary Computation methods versus the Levenberg–Marquardt approach A. Piotrowski & J. Napiorkowski 10.1016/j.jhydrol.2011.06.019
51. An empirical modeling approach to predict and understand phytoplankton dynamics in a reservoir affected by interbasin water transfers R. Fornarelli et al. 10.1002/wrcr.20268
52. Rainfall-runoff modelling using a self-reliant fuzzy inference network with flexible structure T. Chang et al. 10.1016/j.jhydrol.2018.07.074
53. Ideal point error for model assessment in data-driven river flow forecasting C. Dawson et al. 10.5194/hess-16-3049-2012
54. Quantifying the Uncertainties in Data-Driven Models for Reservoir Inflow Prediction X. Zhang et al. 10.1007/s11269-020-02514-7
55. Evaluation of plotless density estimators in different plant density intensities and distribution patterns H. Jamali et al. 10.1016/j.gecco.2020.e01114
56. Support vector regression optimized by meta-heuristic algorithms for daily streamflow prediction A. Malik et al. 10.1007/s00477-020-01874-1
57. An overview of river flood forecasting procedures in Canadian watersheds Z. Zahmatkesh et al. 10.1080/07011784.2019.1601598
58. Moment-based metrics for global sensitivity analysis of hydrological systems A. Dell'Oca et al. 10.5194/hess-21-6219-2017
59. Ensemble‐Based Neural Network Modeling for Hydrologic Forecasts: Addressing Uncertainty in the Model Structure and Input Variable Selection T. Kim et al. 10.1029/2019WR026262
60. Mid- to Long-Term Runoff Prediction Based on Deep Learning at Different Time Scales in the Upper Yangtze River Basin Y. Ren et al. 10.3390/w14111692
61. Coupling soil moisture and precipitation observations for predicting hourly runoff at small catchment scale G. Tayfur et al. 10.1016/j.jhydrol.2013.12.045
62. Quantifying Climate and Catchment Control on Hydrological Drought in the Continental United States G. Konapala & A. Mishra 10.1029/2018WR024620
63. 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
64. Knowledge extraction from trained ANN drought classification model V. Vidyarthi & A. Jain 10.1016/j.jhydrol.2020.124804
65. Fuzzy Logic for Rainfall-Runoff Modelling Considering Soil Moisture G. Tayfur & L. Brocca 10.1007/s11269-015-1012-0
66. 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
67. Challenges in Applying Machine Learning Models for Hydrological Inference: A Case Study for Flooding Events Across Germany L. Schmidt et al. 10.1029/2019WR025924
68. Comparative analysis of random forest, exploratory regression, and structural equation modeling for screening key environmental variables in evaluating rangeland above-ground biomass N. Kaveh et al. 10.1016/j.ecoinf.2023.102251
69. 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
70. Quantification of the predictive uncertainty of artificial neural network based river flow forecast models K. Kasiviswanathan & K. Sudheer 10.1007/s00477-012-0600-2
71. River ice breakup timing prediction through stacking multi-type model trees W. Sun 10.1016/j.scitotenv.2018.07.001
72. Improving the representation of soil moisture by using a semi‐analytical infiltration model L. Brocca et al. 10.1002/hyp.9766
73. Quantifying the Performances of the Semi-Distributed Hydrologic Model in Parallel Computing—A Case Study J. Kim & J. Ryu 10.3390/w11040823
74. Comparison of different methods for reconstruction of instantaneous peak flow data A. Fathzadeh et al. 10.1080/10798587.2015.1120991
75. Improving predictions of hydrological low-flow indices in ungaged basins using machine learning S. Worland et al. 10.1016/j.envsoft.2017.12.021
76. A comparison of particle swarm optimization and genetic algorithm for daily rainfall-runoff modelling: a case study for Southeast Queensland, Australia M. Jahandideh-Tehrani et al. 10.1007/s11081-020-09538-3
77. Supporting M5 model trees with sensitivity information derived from conceptual hydrological models C. Massmann 10.2166/hydro.2015.111
78. A linguistic decision tree approach to predicting storm surge S. Royston et al. 10.1016/j.fss.2012.10.001
79. 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
80. Methods used for quantifying the prediction uncertainty of artificial neural network based hydrologic models K. Kasiviswanathan & K. Sudheer 10.1007/s00477-016-1369-5
81. Predicting Runoff Signatures Using Regression and Hydrological Modeling Approaches Y. Zhang et al. 10.1029/2018WR023325
82. Machine learning models for streamflow regionalization in a tropical watershed R. Ferreira et al. 10.1016/j.jenvman.2020.111713
83. Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review V. Nourani et al. 10.1016/j.jhydrol.2014.03.057
84. 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
85. 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
86. 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
87. Runoff forecasting using a Takagi–Sugeno neuro-fuzzy model with online learning A. Talei et al. 10.1016/j.jhydrol.2013.02.022
88. Application of rule based methods to predicting storm surge S. Royston et al. 10.1016/j.csr.2012.02.018
89. Rainfall-runoff Modeling Using Dynamic Evolving Neural Fuzzy Inference System with Online Learning C. Kwin et al. 10.1016/j.proeng.2016.07.518
90. 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
91. Improving ANN-based streamflow estimation models for the Upper Indus Basin using satellite-derived snow cover area M. Hassan & I. Hassan 10.1007/s11600-020-00491-4
92. Long-range precipitation forecasts using paleoclimate reconstructions in the western United States C. Carrier et al. 10.1007/s11629-014-3360-2
93. Frequency based imputation of precipitation F. Dikbas 10.1007/s00477-016-1356-x
94. Flash Flood Susceptibility Modeling and Magnitude Index Using Machine Learning and Geohydrological Models: A Modified Hybrid Approach S. Elmahdy et al. 10.3390/rs12172695
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97. Using Machine Learning for Prediction of Saturated Hydraulic Conductivity and Its Sensitivity to Soil Structural Perturbations S. Araya & T. Ghezzehei 10.1029/2018WR024357
98. A Model Tree Generator (MTG) Framework for Simulating Hydrologic Systems: Application to Reservoir Routing M. Rahnamay Naeini et al. 10.3390/w12092373
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100. Wave load formulae for prediction of wave-induced forces on a slender pile within pile groups L. Bonakdar et al. 10.1016/j.coastaleng.2015.05.003
101. Evaluation of random forests and Prophet for daily streamflow forecasting G. Papacharalampous & H. Tyralis 10.5194/adgeo-45-201-2018
102. 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
103. Comparative Analysis of River Flow Modelling by Using Supervised Learning Technique S. Ismail et al. 10.1088/1742-6596/995/1/012045
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105. 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
106. 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
107. River flow forecasting with artificial neural networks using satellite observed precipitation pre-processed with flow length and travel time information: case study of the Ganges river basin M. Akhtar et al. 10.5194/hess-13-1607-2009