60 citations as recorded by crossref.

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3. Prediction and modelling of rainfall–runoff during typhoon events using a physically-based and artificial neural network hybrid model C. Young & W. Liu https://doi.org/10.1080/02626667.2014.959446
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14. A hybrid CNN–RNN model for rainfall–runoff modeling in the Potteruvagu watershed of India P. Shekar et al. https://doi.org/10.1002/clen.202300341
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27. 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
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29. Modeling Slump of Ready Mix Concrete Using Genetically Evolved Artificial Neural Networks V. Chandwani et al. https://doi.org/10.1155/2014/629137
30. Enhancing river flow prediction accuracy in data scarce catchments using a time varying ensemble deep learning approach A. Tumushabe et al. https://doi.org/10.1016/j.jhydrol.2026.136387
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34. Small Town Population Forecasting and Planning Method Based on Genetic Algorithm and BP Neural Network N. Shen https://doi.org/10.1142/S0129156425401068
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37. Error Correction of Rainfall-Runoff Models With the ARMAsel Program P. Broersen https://doi.org/10.1109/TIM.2007.908252
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43. 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
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45. A metamodeling with CFD method for hydrodynamic optimisations of deflectors on a multi-wing trawl door G. Wang et al. https://doi.org/10.1016/j.oceaneng.2021.109045
46. 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
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50. Bioengineering for multiple PAHs degradation using process centric and data centric approaches H. Gosai et al. https://doi.org/10.1016/j.chemolab.2018.04.019
51. Review of Nitrogen Compounds Prediction in Water Bodies Using Artificial Neural Networks and Other Models P. Kumar et al. https://doi.org/10.3390/su12114359
52. Surface Water Quality Model: Impacts of Influential Variables P. Yousefi et al. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000900
53. Short‐term flood forecasting with a neurofuzzy model P. Nayak et al. https://doi.org/10.1029/2004WR003562
54. RETRACTED: Machine learning approaches for estimation of sediment settling velocity S. Zhu et al. https://doi.org/10.1016/j.jhydrol.2020.124911
55. 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
56. 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
57. 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
58. A hybrid linear‐neural model for river flow forecasting M. Chetan & K. Sudheer https://doi.org/10.1029/2005WR004072
59. Comparison of soft-computing techniques: Data-driven models for flood forecasting R. Chaudhari et al. https://doi.org/10.3934/environsci.2024037
60. 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