27 citations as recorded by crossref.

1. Storage-Yield Evaluation and Operation of Mula Reservoir, India D. Srivastava & T. Awchi https://doi.org/10.1061/(ASCE)0733-9496(2009)135:6(414)
2. Artificial neural network model for synthetic streamflow generation J. Ahmed & A. Sarma https://doi.org/10.1007/s11269-006-9070-y
3. Disaggregation Modelling of Annual Flows into Daily Streamflows Using a New Approach of the Method of Fragments M. Portela & A. Silva https://doi.org/10.1007/s11269-016-1402-y
4. Stochastic modelling of flow sequences for improved prediction of fluvial flood hazards S. Patidar et al. https://doi.org/10.1144/SP488.4
5. Heuristic Modelling of the Water Resources Management in the Guadalquivir River Basin, Southern Spain I. Pulido-Calvo et al. https://doi.org/10.1007/s11269-011-9912-0
6. Prospecting droughts with stochastic artificial neural networks J. Ochoa-Rivera https://doi.org/10.1016/j.jhydrol.2008.01.006
7. MSGP-LASSO: An improved multi-stage genetic programming model for streamflow prediction A. Mehr & A. Gandomi https://doi.org/10.1016/j.ins.2021.02.011
8. Modeling monthly streamflow in mountainous basin by MARS, GMDH-NN and DENFIS using hydroclimatic data R. Adnan et al. https://doi.org/10.1007/s00521-020-05164-3
9. Techniques and Developments in Stochastic Streamflow Synthesis—A Comprehensive Review S. Studnicka & U. Panu https://doi.org/10.3390/encyclopedia5040198
10. Klang River–Level Forecasting Using ARIMA and ANFIS Models H. Galavi et al. https://doi.org/10.5942/jawwa.2013.105.0106
11. Application of ANN-Based Streamflow Forecasting Model for Agricultural Water Management in the Awash River Basin, Ethiopia D. Edossa & M. Babel https://doi.org/10.1007/s11269-010-9773-y
12. A nonlinear data‐driven model for synthetic generation of annual streamflows K. Sudheer et al. https://doi.org/10.1002/hyp.6764
13. A Pareto-optimal moving average multigene genetic programming model for daily streamflow prediction A. Danandeh Mehr & E. Kahya https://doi.org/10.1016/j.jhydrol.2017.04.045
14. Previsão de secas na primavera em Portugal Continental com base em indicadores climáticos de larga escala J. Santos et al. https://doi.org/10.4995/ia.2015.4109
15. Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions H. Maier et al. https://doi.org/10.1016/j.envsoft.2010.02.003
16. Performance of multisite streamflow stochastic generation approaches for a multi-reservoir system Y. Ma et al. https://doi.org/10.1007/s00477-024-02672-9
17. Real-Time Flood Control by Tree-Based Model Predictive Control Including Forecast Uncertainty: A Case Study Reservoir in Turkey G. Uysal et al. https://doi.org/10.3390/w10030340
18. Um desenvolvimento adicional do método dos fragmentos. Aplicação à desagregação de escoamentos anuais em escoamento diários M. Manuela Portela & A. Silva https://doi.org/10.1080/23863781.2017.1332822
19. Complex Neurofuzzy ARIMA Forecasting—A New Approach Using Complex Fuzzy Sets C. Li & T. Chiang https://doi.org/10.1109/TFUZZ.2012.2226890
20. Generation of synthetic benchmark electrical load profiles using publicly available load and weather data G. Pillai et al. https://doi.org/10.1016/j.ijepes.2014.03.005
21. Artificial neural network based hybrid modeling approach for flood inundation modeling S. Xie et al. https://doi.org/10.1016/j.jhydrol.2020.125605
22. Current awareness https://doi.org/10.1002/hyp.5099
23. Daily streamflow modelling using autoregressive moving average and artificial neural networks models: case study ofÇoruh basin,Turkey İ. Can et al. https://doi.org/10.1111/j.1747-6593.2012.00337.x
24. Runoff Forecast for the Flood Season Based on Physical Factors and Their Effect Process and Its Application in the Second Songhua River Basin, China Y. Cidan et al. https://doi.org/10.3390/su141710627
25. Current awareness https://doi.org/10.1002/hyp.5126
26. Current State of Advances in Quantification and Modeling of Hydrological Droughts T. Sharma & U. Panu https://doi.org/10.3390/w16050729
27. Successive-station monthly streamflow prediction using different artificial neural network algorithms A. Danandeh Mehr et al. https://doi.org/10.1007/s13762-014-0613-0