58 citations as recorded by crossref.

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26. Climate models and hydrological parameter uncertainties in climate change impacts on monthly runoff and daily flow duration curve of a Mediterranean catchment H. Sellami et al. https://doi.org/10.1080/02626667.2015.1040801
27. Streamflow forecasting using least-squares support vector machines A. Shabri & . Suhartono https://doi.org/10.1080/02626667.2012.714468
28. Modeling Slump of Ready Mix Concrete Using Genetically Evolved Artificial Neural Networks V. Chandwani et al. https://doi.org/10.1155/2014/629137
29. Artificial Neural Network Modeling of the Water Quality Index Using Land Use Areas as Predictors N. Gazzaz et al. https://doi.org/10.2175/106143014X14062131179276
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32. Small Town Population Forecasting and Planning Method Based on Genetic Algorithm and BP Neural Network N. Shen https://doi.org/10.1142/S0129156425401068
33. Application of neural network and time series modeling to study the suitability of drain water quality for irrigation: a case study from Egypt M. Abdel-Fattah et al. https://doi.org/10.1007/s11356-020-10543-3
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35. Error Correction of Rainfall-Runoff Models With the ARMAsel Program P. Broersen https://doi.org/10.1109/TIM.2007.908252
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37. Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition W. Wang et al. https://doi.org/10.1016/j.envres.2015.02.002
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41. Development of Process and Data Centric Inference System for Enhanced Production of L-Asparaginase from Halotolerant Bacillus licheniformis PPD37 P. Patel et al. https://doi.org/10.1007/s12010-021-03707-5
42. Comparison of artificial neural networks and autoregressive model for inflows forecasting of Roseires Reservoir for better prediction of irrigation water supply in Sudan M. Abdellatif et al. https://doi.org/10.1080/15715124.2014.1003381
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44. Toward Optimal Rainfall for Flood Prediction in Headwater Basins—Orographic QPE Error Modeling Using Machine Learning M. Liao & A. Barros https://doi.org/10.1029/2023WR034456
45. Grey model for prediction of pore pressure change M. Mohammed et al. https://doi.org/10.1007/s12665-009-0287-y
46. Modeling slump of ready mix concrete using genetic algorithms assisted training of Artificial Neural Networks V. Chandwani et al. https://doi.org/10.1016/j.eswa.2014.08.048
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51. Short‐term flood forecasting with a neurofuzzy model P. Nayak et al. https://doi.org/10.1029/2004WR003562
52. RETRACTED: Machine learning approaches for estimation of sediment settling velocity S. Zhu et al. https://doi.org/10.1016/j.jhydrol.2020.124911
53. Prediction of mechanical properties of non-welded and moderately welded ignimbrite using physical properties, ultrasonic pulse velocity, and point load index tests A. Binal https://doi.org/10.1144/1470-9326/07-067
54. Application of Machine Learning Techniques in Rainfall–Runoff Modelling of the Soan River Basin, Pakistan M. Khan et al. https://doi.org/10.3390/w13243528
55. Comparing the Selected Transfer Functions and Local Optimization Methods for Neural Network Flood Runoff Forecast P. Maca et al. https://doi.org/10.1155/2014/782351
56. A hybrid linear‐neural model for river flow forecasting M. Chetan & K. Sudheer https://doi.org/10.1029/2005WR004072
57. Comparison of soft-computing techniques: Data-driven models for flood forecasting R. Chaudhari et al. https://doi.org/10.3934/environsci.2024037
58. Data-driven modelling approaches for socio-hydrology: opportunities and challenges within the Panta Rhei Science Plan N. Mount et al. https://doi.org/10.1080/02626667.2016.1159683