21 citations as recorded by crossref.

1. Genetic programming approach for flood routing in natural channels C. Sivapragasam et al. https://doi.org/10.1002/hyp.6628
2. Application of surrogate artificial intelligent models for real‐time flood routing H. Ghalkhani et al. https://doi.org/10.1111/j.1747-6593.2012.00344.x
3. On the Use of Intelligent Systems for the Modelling of Financial Literacy Parameters H. Tawfik et al. https://doi.org/10.4018/jitr.2009062902
4. Predicting Outflow Hydrographs of Potential Dike Breaches in a Bifurcating River System Using NARX Neural Networks A. Bomers https://doi.org/10.3390/hydrology8020087
5. Prediction of groundwater drawdown using artificial neural networks V. Gholami & H. Sahour https://doi.org/10.1007/s11356-021-18115-9
6. Spatial Modeling of Natural Phenomena and Events with Artificial Neural Networks and GIS S. Noack et al. https://doi.org/10.4018/jagr.2012010101
7. Application of artificial neural network for predicting water levels in Hooghly estuary, India K. Bhar & S. Bakshi https://doi.org/10.2166/h2oj.2020.041
8. An efficient flash flood forecasting system for the un-gaged Meghna basin using open source platform Delft-FEWS M. Chowdhury et al. https://doi.org/10.1016/j.envsoft.2022.105614
9. Flood inference simulation using surrogate modelling for the Yellow River multiple reservoir system M. Castro-Gama et al. https://doi.org/10.1016/j.envsoft.2014.02.002
10. Application of the SVR-NSGAII to Hydrograph Routing in Open Channels M. Aboutalebi et al. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000969
11. River flood routing using artificial neural networks A. Tawfik https://doi.org/10.1016/j.asej.2022.101904
12. A Hybrid Framework for Rapidly Locating Transition Zones: A Comparison of Event‐ and Response‐Based Return Water Levels in the Suwannee River FL R. Jane et al. https://doi.org/10.1029/2022WR032481
13. Simulation of rainfall-runoff process using an artificial neural network (ANN) and field plots data V. Gholami & H. Sahour https://doi.org/10.1007/s00704-021-03817-4
14. Comparison of parametric and non-parametric estimations of the annual date of positive water temperature onset A. Daigle et al. https://doi.org/10.1016/j.jhydrol.2010.06.032
15. Comparison of three artificial intelligence techniques for discharge routing R. Khatibi et al. https://doi.org/10.1016/j.jhydrol.2011.03.007
16. 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
17. Hybrid bio-inspired optimization with artificial neural networks for efficient flood routing in watershed management N. Elshaboury et al. https://doi.org/10.1007/s42108-025-00376-6
18. Historic Flood Reconstruction With the Use of an Artificial Neural Network A. Bomers et al. https://doi.org/10.1029/2019WR025656
19. Machine learning meets individual-based modelling: Self-organising feature maps for the analysis of below-ground competition among plants R. Peters et al. https://doi.org/10.1016/j.ecolmodel.2015.10.014
20. Review of Machine Learning Methods for River Flood Routing L. Li & K. Jun https://doi.org/10.3390/w16020364
21. Regional low‐flow frequency analysis using single and ensemble artificial neural networks T. Ouarda & C. Shu https://doi.org/10.1029/2008WR007196