77 citations as recorded by crossref.

1. Uncertainty Analysis of Multi-Model Flood Forecasts E. Plate & K. Shahzad 10.3390/w7126654
2. Application of surrogate artificial intelligent models for real‐time flood routing H. Ghalkhani et al. 10.1111/j.1747-6593.2012.00344.x
3. Ensemble flood forecasting based on a coupled atmospheric-hydrological modeling system with data assimilation J. Tian et al. 10.1016/j.atmosres.2019.03.029
4. 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
5. Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques C. Wu et al. 10.1016/j.jhydrol.2010.05.040
6. Flood forecasting at the Dadu River in China based on ANN L. Feng 10.1111/j.1468-0394.2012.00644.x
7. Online multistep-ahead inundation depth forecasts by recurrent NARX networks H. Shen & L. Chang 10.5194/hess-17-935-2013
8. Artificial neural network for forecasting rainfall pattern in Prince Edward Island, Canada S. Khedhiri 10.1080/00207233.2014.961311
9. A national scale flood hazard mapping methodology: The case of Greece – Protection and adaptation policy approaches N. Kourgialas & G. Karatzas 10.1016/j.scitotenv.2017.05.197
10. Propagation of uncertainties in coupled hydro-meteorological forecasting systems: A stochastic approach for the assessment of the total predictive uncertainty R. Hostache et al. 10.1016/j.atmosres.2010.09.014
11. A Brief review of flood forecasting techniques and their applications S. Jain et al. 10.1080/15715124.2017.1411920
12. Clustering of heterogeneous precipitation fields for the assessment and possible improvement of lumped neural network models for streamflow forecasts N. Lauzon et al. 10.5194/hess-10-485-2006
13. Estimation of parameters of the transient storage model by means of multi-layer perceptron neural networks / Estimation des paramètres du modèle de transport TSM au moyen de réseaux de neurones perceptrons multi-couches P. ROWIŃSKI & A. PIOTROWSKI 10.1623/hysj.53.1.165
14. Forecasting histogram time series with k-nearest neighbours methods J. Arroyo & C. Maté 10.1016/j.ijforecast.2008.07.003
15. Generation of rainfall data series by using the Markov Chain model in three selected sites in the Kurdistan Region, Iraq E. Hajani & G. Sarma 10.1007/s43503-023-00014-2
16. Space-time disaggregation of precipitation and temperature across different climates and spatial scales K. Breinl & G. Di Baldassarre 10.1016/j.ejrh.2018.12.002
17. Impact of EMD decomposition and random initialisation of weights in ANN hindcasting of daily stream flow series: An empirical examination G. Napolitano et al. 10.1016/j.jhydrol.2011.06.015
18. Rainfall-runoff modelling using improved machine learning methods: Harris hawks optimizer vs. particle swarm optimization Y. Tikhamarine et al. 10.1016/j.jhydrol.2020.125133
19. Estimating the Uncertainty of Hydrological Predictions through Data-Driven Resampling Techniques A. Sikorska et al. 10.1061/(ASCE)HE.1943-5584.0000926
20. Performance of conceptual and black-box models in flood warning systems M. Banihabib & R. Revelli 10.1080/23311916.2015.1127798
21. Streamflow simulation methods for ungauged and poorly gauged watersheds A. Loukas & L. Vasiliades 10.5194/nhess-14-1641-2014
22. A Hybrid Approach to Improve Flood Forecasting by Combining a Hydrodynamic Flow Model and Artificial Neural Networks L. Li & K. Jun 10.3390/w14091393
23. Entropy-based discrimination between translated Chinese and original Chinese using data mining techniques K. Liu et al. 10.1371/journal.pone.0265633
24. Improving flow forecasting by error correction modelling in altered catchment conditions F. Pianosi et al. 10.1002/hyp.9788
25. Extreme value analysis and forecasting of maximum precipitation amounts in the western Black Sea subregion of Turkey C. Yozgatlıgil & M. Türkeş 10.1002/joc.5738
26. Comparative Analysis of Artificial Neural Network (ANN) and Wavelet Integrated Artificial Neural Network (W-ANN) Approaches for Rainfall Modeling of Southern Rajasthan, India V. Paradkar & H. Mittal 10.12944/CWE.18.3.17
27. A hybrid model coupling process-driven and data-driven models for improved real-time flood forecasting C. Xu et al. 10.1016/j.jhydrol.2024.131494
28. Long short-term memory networks for enhancing real-time flood forecasts: a case study for an underperforming hydrologic model S. Gegenleithner et al. 10.5194/hess-29-1939-2025
29. 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
30. Improved Rainfall Prediction Using Combined Pre-Processing Methods and Feed-Forward Neural Networks D. Tran Anh et al. 10.3390/j2010006
31. A New Rainfall-Runoff Model Using Improved LSTM with Attentive Long and Short Lag-Time X. Chen et al. 10.3390/w14050697
32. Short-term daily precipitation forecasting with seasonally-integrated autoencoder D. Ponnoprat 10.1016/j.asoc.2021.107083
33. Real-Time Forecast of Reservoir Inflow Hydrographs Incorporating Terrain and Monsoon Effects during Typhoon Invasion by Novel Intelligent Numerical-Statistic Impulse Techniques N. Hsu et al. 10.1061/(ASCE)HE.1943-5584.0001142
34. Nierji reservoir flood forecasting based on a Data-Based Mechanistic methodology G. Wei et al. 10.1016/j.jhydrol.2018.10.026
35. Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling K. Jafarzadegan et al. 10.1029/2022RG000788
36. Probabilistic nonlinear prediction of river flows S. Tamea et al. 10.1029/2005WR004136
37. Multistep ahead streamflow forecasting: Role of calibration data in conceptual and neural network modeling E. Toth & A. Brath 10.1029/2006WR005383
38. Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks Y. Chiang et al. 10.5194/hess-15-185-2011
39. ANN-based mapping of monthly reference crop evapotranspiration by using altitude, latitude and longitude data in Fars province, Iran S. Shirin Manesh et al. 10.1007/s10668-013-9465-x
40. Current awareness 10.1002/hyp.5126
41. Combining semi-distributed process-based and data-driven models in flow simulation: a case study of the Meuse river basin G. Corzo et al. 10.5194/hess-13-1619-2009
42. Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: Methodology development and investigation using toy models G. Papacharalampous et al. 10.1016/j.advwatres.2019.103471
43. Coupling the Xinanjiang model and wavelet-based random forests method for improved daily streamflow simulation J. Wang et al. 10.2166/hydro.2021.111
44. Current awareness 10.1002/hyp.5099
45. Flood forecasting for River Mekong with data‐based models K. Shahzad & E. Plate 10.1002/2013WR015072
46. 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
47. A simple approach for improving spatial interpolation of rainfall using ANN C. Sivapragasam et al. 10.1007/s00703-010-0090-z
48. Runoff forecasting for an asphalt plane by Artificial Neural Networks and comparisons with kinematic wave and autoregressive moving average models L. Chua & T. Wong 10.1016/j.jhydrol.2010.11.030
49. A combined deep CNN-RNN network for rainfall-runoff modelling in Bardha Watershed, India P. Shekar et al. 10.1016/j.aiig.2024.100073
50. Coupling SWAT and Transformer Models for Enhanced Monthly Streamflow Prediction J. Tao et al. 10.3390/su16198699
51. Baseflow separation techniques for modular artificial neural network modelling in flow forecasting G. CORZO & D. SOLOMATINE 10.1623/hysj.52.3.491
52. Comparison of Neural Network and K-Nearest Neighbor Methods in Daily Flow Forecasting A. Eskandarin et al. 10.3923/jas.2010.1006.1010
53. A web based tool for operational real-time flood forecasting using data assimilation to update hydraulic states M. Mure-Ravaud et al. 10.1016/j.envsoft.2016.06.002
54. A novel self-adjusting weight approximation procedure to minimize non-identical seasonal effects in multimodel ensemble for accurate twenty-first century drought assessment R. Naz & Z. Ali 10.1007/s00477-024-02689-0
55. Classified real-time flood forecasting by coupling fuzzy clustering and neural network M. REN et al. 10.1016/S1001-6279(10)60033-9
56. River flow forecasting with artificial neural networks using satellite observed precipitation pre-processed with flow length and travel time information: case study of the Ganges river basin M. Akhtar et al. 10.5194/hess-13-1607-2009
57. Performance comparison of filtering methods on modelling and forecasting the total precipitation amount: a case study for Muğla in Turkey S. Neslihanoglu et al. 10.2166/wcc.2021.332
58. Projected precipitation and streamflow under SRES and RCP emission scenarios in the Songhuajiang River basin, China B. Su et al. 10.1016/j.quaint.2014.03.049
59. Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting R. Abrahart et al. 10.1177/0309133312444943
60. A lumped conceptual approach for modeling hydrological processes: the case of Scopia catchment area, Central Greece N. Charizopoulos et al. 10.1007/s12665-017-6967-0
61. 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)
62. Evolution of Flood Prediction and Forecasting Models for Flood Early Warning Systems: A Scoping Review N. Byaruhanga et al. 10.3390/w16131763
63. Coupled Heuristic Prediction of Long Lead-Time Accumulated Total Inflow of a Reservoir during Typhoons Using Deterministic Recurrent and Fuzzy Inference-Based Neural Network C. Huang et al. 10.3390/w7116516
64. ERM model analysis for adaptation to hydrological model errors M. Baymani-Nezhad & D. Han 10.1007/s11600-018-0137-y
65. PI-SIGMA NEURAL NETWORK FOR A ONE-STEP-AHEAD TEMPERATURE FORECASTING N. HUSAINI et al. 10.1142/S1469026814500230
66. A comparison and uncertainty assessment of system analysis techniques for short‐term quantitative precipitation nowcasting based on radar images L. Montanari et al. 10.1029/2005JD006729
67. 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
68. Real‐time deployment of artificial neural network forecasting models: Understanding the range of applicability G. Bowden et al. 10.1029/2012WR011984
69. Development of a sequential Artificial Neural Network for predicting river water levels based on Brahmaputra and Ganges water levels M. Siddiquee & M. Hossain 10.1007/s00521-015-1871-6
70. Monthly River Forecasting Using Instance-Based Learning Methods and Climatic Parameters M. Yazdani & A. Zolfaghari 10.1061/(ASCE)HE.1943-5584.0001490
71. 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
72. Performance Assessment of the Linear, Nonlinear and Nonparametric Data Driven Models in River Flow Forecasting A. Ahani et al. 10.1007/s11269-017-1792-5
73. A Hybrid Model to Predict Monthly Streamflow Using Neighboring Rivers Annual Flows A. Al-Juboori 10.1007/s11269-020-02757-4
74. Generating Ensemble Streamflow Forecasts: A Review of Methods and Approaches Over the Past 40 Years M. Troin et al. 10.1029/2020WR028392
75. Prediction of rainfall time series using modular soft computingmethods C. Wu & K. Chau 10.1016/j.engappai.2012.05.023
76. A hybrid linear–nonlinear approach to predict the monthly rainfall over the Urmia Lake watershed using wavelet-SARIMAX-LSSVM conjugated model J. Farajzadeh & F. Alizadeh 10.2166/hydro.2017.013
77. Data-driven modelling approaches for socio-hydrology: opportunities and challenges within the Panta Rhei Science Plan N. Mount et al. 10.1080/02626667.2016.1159683