71 citations as recorded by crossref.

1. River flow modelling for flood prediction using artificial neural network in ungauged Perkerra catchment, Baringo County, Kenya S. Chebii et al. 10.2166/wpt.2022.034
2. 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
3. Application of Regularized Dynamic System Response Curve for Runoff Correction Based on HBV Model: Case Study of Shiquan Catchment, China J. Wang et al. 10.1061/(ASCE)HE.1943-5584.0002168
4. Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling S. Razavi 10.1016/j.envsoft.2021.105159
5. River Flow Forecasting Using Neural Networks and Auto-Calibrated NAM Model with Shuffled Complex Evolution M. Zakermoshf et al. 10.3923/jas.2008.1487.1494
6. Short-term water demand forecasting using hybrid supervised and unsupervised machine learning model M. Bata et al. 10.1186/s40713-020-00020-y
7. Estimation of predictive hydrological uncertainty using quantile regression: examples from the National Flood Forecasting System (England and Wales) A. Weerts et al. 10.5194/hess-15-255-2011
8. A physically based and machine learning hybrid approach for accurate rainfall-runoff modeling during extreme typhoon events C. Young et al. 10.1016/j.asoc.2016.12.052
9. Forecasting the Level of Reservoirs Using Multiple Input Fuzzification in ANFIS N. Valizadeh & A. El-Shafie 10.1007/s11269-013-0349-5
10. Comparison of ANN model and GIS tools for delineation of groundwater potential zones, Fincha Catchment, Abay Basin, Ethiopia H. Tamiru & M. Wagari 10.1080/10106049.2021.1946171
11. Identification of parametrically-varying models for the rainfall-runoff relationship in urban drainage networks F. Previdi & M. Lovera 10.3182/20090706-3-FR-2004.00294
12. Effective flood forecasting at higher lead times through hybrid modelling framework C. Kurian et al. 10.1016/j.jhydrol.2020.124945
13. RAINFALL RUNOFF MODELLING USING NEURAL NETWORKS: STATE-OF-THE-ART AND FUTURE RESEARCH NEEDS A. Jain et al. 10.1080/09715010.2009.10514968
14. Comment on “Advances in ungauged streamflow prediction using artificial neural networks” by Besaw et al. [Journal of Hydrology, 386 (2010) 27–37] Y. Dhanesh & K. Sudheer 10.1016/j.jhydrol.2010.12.044
15. Model trees as an alternative to neural networks in rainfall—runoff modelling D. SOLOMATINE & K. DULAL 10.1623/hysj.48.3.399.45291
16. A hybrid approach to monthly streamflow forecasting: Integrating hydrological model outputs into a Bayesian artificial neural network G. Humphrey et al. 10.1016/j.jhydrol.2016.06.026
17. Updating Real-Time Flood Forecasting Using a Fuzzy Rule-Based Model/Mise à Jour de Prévision de Crue en Temps Réel Grâce à un Modèle à Base de Règles Floues P. Yu & S. Chen 10.1623/hysj.50.2.265.61796
18. The use of time series modeling for the determination of rainfall climates of Iran S. Soltani et al. 10.1002/joc.1427
19. Legitimising data-driven models: exemplification of a new data-driven mechanistic modelling framework N. Mount et al. 10.5194/hess-17-2827-2013
20. Auto-Regressive Neural-Network Models for Long Lead-Time Forecasting of Daily Flow M. Banihabib et al. 10.1007/s11269-018-2094-2
21. Combined Hydraulic and Black-Box Models for Flood Forecasting in Urban Drainage Systems M. Bruen & J. Yang 10.1061/(ASCE)1084-0699(2006)11:6(589)
22. 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
23. Stream flow forecasting by artificial neural network (ANN) model trained by real coded genetic algorithm (GA) S. A et al. 10.5917/jagh1987.48.233
24. Potential application of wavelet neural network ensemble to forecast streamflow for flood management K. Kasiviswanathan et al. 10.1016/j.jhydrol.2016.02.044
25. 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
26. Bayesian Theory Based Self-Adapting Real-Time Correction Model for Flood Forecasting J. Wang et al. 10.3390/w8030075
27. Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks P. Maca & P. Pech 10.1155/2016/3868519
28. 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
29. Symbiotic adaptive neuro-evolution applied to rainfall–runoff modelling in northern England C. Dawson et al. 10.1016/j.neunet.2006.01.009
30. Improving real-time inflow forecasting into hydropower reservoirs through a complementary modelling framework A. Gragne et al. 10.5194/hess-19-3695-2015
31. Selected model fusion: an approach for improving the accuracy of monthly streamflow forecasting F. Modaresi et al. 10.2166/hydro.2018.098
32. Interpreting characteristic drainage timescale variability across Kilombero Valley, Tanzania S. Lyon et al. 10.1002/hyp.10304
33. Comparison of four updating models for real-time river flow forecasting L. XIONG & K. O'CONNOR 10.1080/02626660209492964
34. A nonlinear perturbation model based on artificial neural network B. Pang et al. 10.1016/j.jhydrol.2006.09.015
35. A hybrid model enhancing streamflow forecasts in paddy land use-dominated catchments with numerical weather prediction model-based meteorological forcings A. Mohanty et al. 10.1016/j.jhydrol.2024.131225
36. Physics-Guided Long Short-Term Memory Network for Streamflow and Flood Simulations in the Lancang–Mekong River Basin B. Liu et al. 10.3390/w14091429
37. Generating Ensemble Streamflow Forecasts: A Review of Methods and Approaches Over the Past 40 Years M. Troin et al. 10.1029/2020WR028392
38. Random forests-based error-correction of streamflow from a large-scale hydrological model: Using model state variables to estimate error terms Y. Shen et al. 10.1016/j.cageo.2021.105019
39. Multi-source error correction for flood forecasting based on dynamic system response curve method Z. Liang et al. 10.1016/j.jhydrol.2020.125908
40. A Hybrid Approach to Improve Flood Forecasting by Combining a Hydrodynamic Flow Model and Artificial Neural Networks L. Li & K. Jun 10.3390/w14091393
41. Flood prediction in southern strip of Caspian Sea watershed S. Chavoshi et al. 10.1134/S0097807813060122
42. 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
43. Prediction and modelling of rainfall–runoff during typhoon events using a physically-based and artificial neural network hybrid model C. Young & W. Liu 10.1080/02626667.2014.959446
44. Current Awareness 10.1002/hyp.5035
45. Enhancing MIKE11 Updating Kernel and Evaluating Its Performance Using Numerical Experiments M. Saddagh & M. Abedini 10.1061/(ASCE)HE.1943-5584.0000427
46. Use of Machine Learning Methods to Reduce Predictive Error of Groundwater Models T. Xu et al. 10.1111/gwat.12061
47. Updating real‐time flood forecasts via the dynamic system response curve method W. Si et al. 10.1002/2015WR017234
48. Genetic algorithm and fuzzy neural networks combined with the hydrological modeling system for forecasting watershed runoff discharge C. Young et al. 10.1007/s00521-015-1832-0
49. The data‐driven approach as an operational real‐time flood forecasting model P. Khac‐Tien Nguyen & L. Hock‐Chye Chua 10.1002/hyp.8347
50. A review on statistical postprocessing methods for hydrometeorological ensemble forecasting W. Li et al. 10.1002/wat2.1246
51. Applicability of Galway River Flow Forecasting and Modeling System (GFFMS) for Lake Tana Basin, Ethiopia T. Dessalegn et al. 10.4236/jwarp.2017.912084
52. Coevolution of machine learning and process‐based modelling to revolutionize Earth and environmental sciences: A perspective S. Razavi et al. 10.1002/hyp.14596
53. Runoff Analysis for a Small Watershed of Tono Area Japan by Back Propagation Artificial Neural Network with Seasonal Data A. Sohail et al. 10.1007/s11269-006-9141-0
54. Evaluation of Maximum a Posteriori Estimation as Data Assimilation Method for Forecasting Infiltration-Inflow Affected Urban Runoff with Radar Rainfall Input J. Pedersen et al. 10.3390/w8090381
55. Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting R. Abrahart et al. 10.1177/0309133312444943
56. A Novel Hybrid Approach Based on Instance Based Learning Classifier and Rotation Forest Ensemble for Spatial Prediction of Rainfall-Induced Shallow Landslides using GIS Q. Nguyen et al. 10.3390/su9050813
57. A Bayesian joint probability post-processor for reducing errors and quantifying uncertainty in monthly streamflow predictions P. Pokhrel et al. 10.5194/hess-17-795-2013
58. A dual-pass error-correction technique for forecasting streamflow T. Pagano et al. 10.1016/j.jhydrol.2011.05.036
59. Optimal level of wavelet decomposition for daily inflow forecasting P. Freire & C. Santos 10.1007/s12145-020-00496-z
60. Comparison between NARX-NN and HEC-HMS models to simulate Wadi Seghir catchment runoff events in Algerian northern I. Kadri et al. 10.1080/15715124.2021.2016781
61. Accuracy Enhancement for Forecasting Water Levels of Reservoirs and River Streams Using a Multiple-Input-Pattern Fuzzification Approach N. Valizadeh et al. 10.1155/2014/432976
62. Embedding machine learning techniques into a conceptual model to improve monthly runoff simulation: A nested hybrid rainfall-runoff modeling U. Okkan et al. 10.1016/j.jhydrol.2021.126433
63. Improving real-time flood forecasting updating through a complete and non-excessive precipitation adjustment S. Xu et al. 10.1016/j.jhydrol.2024.132538
64. Application of linear and nonlinear techniques in river flow forecasting in the Kilombero River basin, Tanzania / Application de techniques linéaires et non-linéaires à la prévision des débits dans le bassin de la Rivière Kilombero, en Tanzanie D. Yawson et al. 10.1623/hysj.2005.50.5.783
65. Review of methods for effective forecasting of river runoff characteristics in mountain and semi-mountain areas E. Gaidukova et al. 10.1088/1755-1315/867/1/012006
66. Convective Oxygen Transport in a Constructed Wetland Pond: Mechanism, Measurements and Modelling by Multilayer Perceptrons B. SCHMID et al. 10.1081/ESE-200055681
67. Application of ANN and HEC-RAS model for flood inundation mapping in lower Baro Akobo River Basin, Ethiopia H. Tamiru & M. Dinka 10.1016/j.ejrh.2021.100855
68. Self‐organizing maps with multiple input‐output option for modeling the Richards equation and its inverse solution N. Schütze et al. 10.1029/2004WR003630
69. Development of a Regularized Dynamic System Response Curve for Real-Time Flood Forecasting Correction Y. Sun et al. 10.3390/w10040450
70. Machine-learning and HEC-RAS integrated models for flood inundation mapping in Baro River Basin, Ethiopia H. Tamiru & M. Wagari 10.1007/s40808-021-01175-8
71. Managing uncertainty in hydrological models using complementary models A. ABEBE & R. PRICE 10.1623/hysj.48.5.679.51450