66 citations as recorded by crossref.

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43. Output updating of a physically based model for gauged and ungauged sites of the Upper Thames River watershed P. Jeevaragagam & S. Simonovic 10.2478/johh-2023-0019
44. 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
45. Assessment of artificial neural network models based on the simulation of groundwater contaminant transport J. Pal & D. Chakrabarty 10.1007/s10040-020-02180-4
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50. Linear and nonlinear ensemble deep learning models for karst spring discharge forecasting R. Zhou & Y. Zhang 10.1016/j.jhydrol.2023.130394
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54. The need for operational reasoning in data‐driven rating curve prediction of suspended sediment N. Mount et al. 10.1002/hyp.8439
55. Assessment of the Real Estate Market Value in the European Market by Artificial Neural Networks Application J. Ćetković et al. 10.1155/2018/1472957
56. Discussion of “Evapotranspiration modelling using support vector machines” R. Abrahart et al. 10.1080/02626667.2010.529448
57. Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia A. El-Shafie et al. 10.5194/hess-16-1151-2012
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59. Disaggregated monthly SWAT+ model versus daily SWAT+ model for estimating environmental flows in Peninsular Spain G. Castellanos-Osorio et al. 10.1016/j.jhydrol.2023.129837
60. Hydrologically Informed Machine Learning for Rainfall‐Runoff Modeling: A Genetic Programming‐Based Toolkit for Automatic Model Induction J. Chadalawada et al. 10.1029/2019WR026933
61. Neural network modelling of non-linear hydrological relationships R. Abrahart & L. See 10.5194/hess-11-1563-2007
62. The need for operational reasoning in data‐driven rating curve prediction of suspended sediment N. Mount et al. 10.1002/hyp.8439
63. Modeling Suspended Sediment Using Artificial Neural Networks and TRMM-3B42 Version 7 Rainfall Dataset D. Kumar et al. 10.1061/(ASCE)HE.1943-5584.0001082
64. A Comparison of ANN and HSPF Models for Runoff Simulation in Balkhichai River Watershed, Iran F. Amirhossien et al. 10.4236/ajcc.2015.43016
65. A comparison of ANN and HSPF models for runoff simulation in Gharehsoo River watershed, Iran K. Javan et al. 10.1007/s40808-015-0042-1
66. Artificial neural networks in proglacial discharge simulation: application and efficiency analysis in comparison to the multivariate regression; a case study of waldemar river (svalbard) M. Nowak & I. Sobota 10.1111/geoa.12089