79 citations as recorded by crossref.

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36. WD-HRNN-BiGRU: A Novel Spatiotemporal Sequence Multi-Value Prediction Model Z. Zhang et al. https://doi.org/10.1007/s11269-026-04541-2
37. Fuzzy neural networks for water level and discharge forecasting with uncertainty S. Alvisi & M. Franchini https://doi.org/10.1016/j.envsoft.2010.10.016
38. Assessment of the Real Estate Market Value in the European Market by Artificial Neural Networks Application J. Ćetković et al. https://doi.org/10.1155/2018/1472957
39. Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia A. El-Shafie et al. https://doi.org/10.5194/hess-16-1151-2012
40. Hydrologically Informed Machine Learning for Rainfall‐Runoff Modeling: A Genetic Programming‐Based Toolkit for Automatic Model Induction J. Chadalawada et al. https://doi.org/10.1029/2019WR026933
41. Leveraging machine learning for prediction and optimization of texture properties of sustainable activated carbon derived from waste materials A. Ibrahim & M. Hussein https://doi.org/10.1038/s41598-025-95061-3
42. Exploring the accuracy of Random Forest and Multiple Regression models to predict rill detachment in soils under different plant species and soil treatments in deforested lands M. Parhizkar et al. https://doi.org/10.1007/s40808-023-01919-8
43. Improved Convolutional Neural Network and its Application in Non-Periodical Runoff Prediction Y. Xu et al. https://doi.org/10.1007/s11269-022-03346-3
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45. Hybrid Optimization Rainfall-Runoff Simulation Based on Xinanjiang Model and Artificial Neural Network X. Song et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000548
46. A simulation of the rainfall-runoff process using artificial neural network and HEC-HMS model in forest lands V. Gholami & M. Khaleghi https://doi.org/10.17221/90/2020-JFS
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48. Deep learning in hydrology and water resources disciplines: concepts, methods, applications, and research directions K. Tripathy & A. Mishra https://doi.org/10.1016/j.jhydrol.2023.130458
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50. The Role of Satellite-Based Remote Sensing in Improving Simulated Streamflow: A Review D. Jiang & K. Wang https://doi.org/10.3390/w11081615
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52. Quantile Regression in Regional Frequency Analysis: A Better Exploitation of the Available Information D. Ouali et al. https://doi.org/10.1175/JHM-D-15-0187.1
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55. A Novel Physics-Statistical Coupled Paradigm for Retrieving Integrated Water Vapor Content Based on Artificial Intelligence R. Mei et al. https://doi.org/10.3390/rs15174250
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62. Neural Network Prediction of Reciprocating Friction for X-Type Seals M. Zhao et al. https://doi.org/10.1115/1.4071119
63. Evaluation of soil detachment capacity in rills of forestlands using artificial neural networks and multiple regressions M. Parhizkar et al. https://doi.org/10.1007/s12517-026-12597-y
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68. Soft Computing Approach for Rainfall-runoff Modelling: A Review V. Chandwani et al. https://doi.org/10.1016/j.aqpro.2015.02.133
69. Machine learning methods for empirical streamflow simulation: a comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds J. Shortridge et al. https://doi.org/10.5194/hess-20-2611-2016
70. Assessment of artificial neural network models based on the simulation of groundwater contaminant transport J. Pal & D. Chakrabarty https://doi.org/10.1007/s10040-020-02180-4
71. Evaluating Urban Stream Flooding with Machine Learning, LiDAR, and 3D Modeling M. Bolick et al. https://doi.org/10.3390/w15142581
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79. Disaggregated monthly SWAT+ model versus daily SWAT+ model for estimating environmental flows in Peninsular Spain G. Castellanos-Osorio et al. https://doi.org/10.1016/j.jhydrol.2023.129837