55 citations as recorded by crossref.

1. River stage prediction based on a distributed support vector regression C. Wu et al. 10.1016/j.jhydrol.2008.05.028
2. An error updating system for real-time flood forecasting based on robust procedure L. Qian et al. 10.1007/s12205-013-0483-x
3. A combined rotated general regression neural network method for river flow forecasting S. Yin et al. 10.1080/02626667.2014.944525
4. Short‐ and long‐term flow forecasting in the Rio Grande watershed (Brazil) C. Tucci et al. 10.1002/asl.165
5. Data-driven methods to improve baseflow prediction of a regional groundwater model T. Xu & A. Valocchi 10.1016/j.cageo.2015.05.016
6. Enhancing real-time streamflow forecasts with wavelet-neural network based error-updating schemes and ECMWF meteorological predictions in Variable Infiltration Capacity model T. Nanda et al. 10.1016/j.jhydrol.2019.05.051
7. On modeling the quality of concept mapping toward more intelligent online learning feedback: a fuzzy logic-based approach S. Dias et al. 10.1007/s10209-019-00656-z
8. Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks P. Maca & P. Pech 10.1155/2016/3868519
9. Comparative study of different wavelet based neural network models for rainfall–runoff modeling M. Shoaib et al. 10.1016/j.jhydrol.2014.04.055
10. Understanding hydrological flow paths in conceptual catchment models using uncertainty and sensitivity analysis E. Mockler et al. 10.1016/j.cageo.2015.08.015
11. Improving real-time inflow forecasting into hydropower reservoirs through a complementary modelling framework A. Gragne et al. 10.5194/hess-19-3695-2015
12. Optimal level of wavelet decomposition for daily inflow forecasting P. Freire & C. Santos 10.1007/s12145-020-00496-z
13. Improving efficiencies of flood forecasting during lead times: an operational method and its application in the Baiyunshan Reservoir P. Liu et al. 10.2166/nh.2018.051
14. Historical development of rainfall‐runoff modeling M. Peel & T. McMahon 10.1002/wat2.1471
15. Skill and relative economic value of medium-range hydrological ensemble predictions E. Roulin 10.5194/hess-11-725-2007
16. Rainfall forecasting in upper Indus basin using various artificial intelligence techniques M. Hammad et al. 10.1007/s00477-021-02013-0
17. Value of process understanding in the era of machine learning: A case for recession flow prediction P. Istalkar et al. 10.1016/j.jhydrol.2023.130350
18. 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. 10.1016/j.envsoft.2010.02.003
19. AR-GARCH with Exogenous Variables as a Postprocessing Model for Improving Streamflow Forecasts X. Zha et al. 10.1061/(ASCE)HE.1943-5584.0001955
20. A back-fitting algorithm to improve real-time flood forecasting X. Zhang et al. 10.1016/j.jhydrol.2018.04.051
21. Methodology for Calculating Daily Streamflow of Russian Rivers Using the HBV-96 Runoff Formation Model S. Borshch et al. 10.3103/S1068373923030044
22. A hybrid model of self organizing maps and least square support vector machine for river flow forecasting S. Ismail et al. 10.5194/hess-16-4417-2012
23. Characterization of regional variability of seasonal water balance within Omo-Ghibe River Basin, Ethiopia A. Jillo et al. 10.1080/02626667.2017.1313419
24. Short-range Streamflow Forecasting of the Kama River Based on the HBV Model Application Y. Simonov et al. 10.3103/S1068373921060054
25. Medium-range reservoir inflow predictions based on quantitative precipitation forecasts W. Collischonn et al. 10.1016/j.jhydrol.2007.06.025
26. System response curve correction method of runoff error for real-time flood forecast Q. Li et al. 10.2166/nh.2020.048
27. Improving flow forecasting by error correction modelling in altered catchment conditions F. Pianosi et al. 10.1002/hyp.9788
28. Evaluation of loss models and effect of LU/LC changes on surface runoff in Subarnarekha river basin A. Dandapat & S. Sahoo 10.1080/09715010.2019.1619489
29. Dealing with Uncertainty in Water Distribution System Models: A Framework for Real-Time Modeling and Data Assimilation C. Hutton et al. 10.1061/(ASCE)WR.1943-5452.0000325
30. Current awareness 10.1002/hyp.6195
31. Russian Rivers Streamflow Forecasting Using Hydrograph Extrapolation Method S. Borsch et al. 10.3390/hydrology9010001
32. Recursively updating the error forecasting scheme of a complementary modelling framework for improved reservoir inflow forecasts A. Gragne et al. 10.1016/j.jhydrol.2015.05.039
33. Coupling the k-nearest neighbor procedure with the Kalman filter for real-time updating of the hydraulic model in flood forecasting K. Liu et al. 10.1016/j.ijsrc.2016.02.002
34. Real-Time Flood Forecasting System: Case Study of Hsia-Yun Watershed, Taiwan J. Huang et al. 10.1061/(ASCE)HE.1943-5584.0001322
35. Error correction-based forecasting of reservoir water levels: Improving accuracy over multiple lead times X. Zhang et al. 10.1016/j.envsoft.2018.02.017
36. Using multiple watershed models to assess the water quality impacts of alternate land development scenarios for a small community A. Sharifi et al. 10.1016/j.catena.2016.11.009
37. Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting R. Abrahart et al. 10.1177/0309133312444943
38. Comparative Study of Three Updating Procedures for Real-Time Flood Forecasting Z. Liu et al. 10.1007/s11269-016-1275-0
39. A dual-pass error-correction technique for forecasting streamflow T. Pagano et al. 10.1016/j.jhydrol.2011.05.036
40. Application of Hydrological and Sediment Modeling with Limited Data in the Abbay (Upper Blue Nile) Basin, Ethiopia B. Abebe et al. 10.3390/hydrology9100167
41. Sequential Method with Incremental Analysis Update to Retrieve Leaf Area Index from Time Series MODIS Reflectance Data J. Jiang et al. 10.3390/rs6109194
42. Short-range Streamflow Forecasting for Russian Rivers Using the HBV-96 Model and the COSMO-Ru System Y. Simonov et al. 10.3103/S1068373923120014
43. Output updating of a physically based model for gauged and ungauged sites of the Upper Thames River watershed P. Jeevaragagam & S. Simonovic 10.2478/johh-2023-0019
44. Automatic state updating for operational streamflow forecasting via variational data assimilation D. Seo et al. 10.1016/j.jhydrol.2009.01.019
45. Error‐correction methods and evaluation of an ensemble based hydrological forecasting system for the Upper Danube catchment K. Bogner & M. Kalas 10.1002/asl.180
46. Integrating a calibrated groundwater flow model with error-correcting data-driven models to improve predictions Y. Demissie et al. 10.1016/j.jhydrol.2008.11.007
47. Two novel error-updating model frameworks for short-to-medium range streamflow forecasting using bias-corrected rainfall inputs: Development and comparative assessment A. Khatun et al. 10.1016/j.jhydrol.2023.129199
48. Use of Machine Learning Methods to Reduce Predictive Error of Groundwater Models T. Xu et al. 10.1111/gwat.12061
49. Calibration of hydrological models for ecologically relevant streamflow predictions: a trade-off between fitting well to data and estimating consistent parameter sets? T. Hallouin et al. 10.5194/hess-24-1031-2020
50. Bounding linear rainfall-runoff models with fractional derivatives applied to a barren catchment of the Jordan Rift Valley K. Unami et al. 10.1016/j.jhydrol.2020.125879
51. Comparison of fuzzy inference systems for streamflow prediction M. ÖZGER 10.1623/hysj.54.2.261
52. Real-time flow forecasting in the absence of quantitative precipitation forecasts: A multi-model approach M. Goswami & K. O’Connor 10.1016/j.jhydrol.2006.10.002
53. Assessing the relative importance of parameter and forcing uncertainty and their interactions in conceptual hydrological model simulations E. Mockler et al. 10.1016/j.advwatres.2016.10.008
54. Real‐time deployment of artificial neural network forecasting models: Understanding the range of applicability G. Bowden et al. 10.1029/2012WR011984
55. Generating Ensemble Streamflow Forecasts: A Review of Methods and Approaches Over the Past 40 Years M. Troin et al. 10.1029/2020WR028392