90 citations as recorded by crossref.

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43. Heuristic Modelling of the Water Resources Management in the Guadalquivir River Basin, Southern Spain I. Pulido-Calvo et al. 10.1007/s11269-011-9912-0
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49. Forecasting Monthly River Flows in Ukraine under Different Climatic Conditions R. Graf & V. Vyshnevskyi 10.3390/resources11120111
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51. Multivariate time series modeling of short-term system scale irrigation demand K. Perera et al. 10.1016/j.jhydrol.2015.11.007
52. Improved Convolutional Neural Network and its Application in Non-Periodical Runoff Prediction Y. Xu et al. 10.1007/s11269-022-03346-3
53. Classification of hydro-meteorological conditions and multiple artificial neural networks for streamflow forecasting E. Toth 10.5194/hess-13-1555-2009
54. Prediction and modelling of rainfall–runoff during typhoon events using a physically-based and artificial neural network hybrid model C. Young & W. Liu 10.1080/02626667.2014.959446
55. Deep learning-based multi-model approach on electron microscopy image of renal biopsy classification J. Zhang & A. Zhang 10.1186/s12882-023-03182-6
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59. Enhancing flood forecasting with the help of processed based calibration J. Cullmann et al. 10.1016/j.pce.2008.03.001
60. A fusion-based neural network methodology for monthly reservoir inflow prediction using MODIS products M. Ghazali et al. 10.1080/02626667.2018.1558365
61. Combining Remotely Sensed Evapotranspiration and an Agroecosystem Model to Estimate Center‐Pivot Irrigation Water Use at High Spatio‐Temporal Resolution J. Zhang et al. 10.1029/2022WR032967
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64. Real-time probabilistic forecasting of flood stages S. Chen & P. Yu 10.1016/j.jhydrol.2007.04.008
65. A wavelet based approach for combining the outputs of different rainfall–runoff models M. Shoaib et al. 10.1007/s00477-016-1364-x
66. Development of an Interdisciplinary Prediction System Combining Sediment Transport Simulation and Ensemble Method H. Ho et al. 10.3390/w13182588
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70. Irrigation Demand Forecasting Using Artificial Neuro-Genetic Networks R. Perea et al. 10.1007/s11269-015-1134-4
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73. Combining semi-distributed process-based and data-driven models in flow simulation: a case study of the Meuse river basin G. Corzo et al. 10.5194/hess-13-1619-2009
74. River ice breakup timing prediction through stacking multi-type model trees W. Sun 10.1016/j.scitotenv.2018.07.001
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85. A stacking neuro-fuzzy framework to forecast runoff from distributed meteorological stations M. Querales et al. 10.1016/j.asoc.2022.108535
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88. Quantitatively identifying the emission sources of pharmaceutically active compounds (PhACs) in the surface water: Method development, verification and application in Huangpu River, China X. Kan et al. 10.1016/j.scitotenv.2021.152783
89. A stacking ensemble learning framework for annual river ice breakup dates W. Sun & B. Trevor 10.1016/j.jhydrol.2018.04.008
90. Development of a modular streamflow model to quantify runoff contributions from different land uses in tropical urban environments using Genetic Programming A. Meshgi et al. 10.1016/j.jhydrol.2015.04.032