96 citations as recorded by crossref.

1. Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques C. Wu et al.
2. An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction N. Ajami et al.
3. Artificial neural network ensembles and their application in pooled flood frequency analysis C. Shu & D. Burn
4. Timing error correction procedure applied to neural network rainfall—runoff modelling R. ABRAHART et al.
5. RESERVOIR DAILY INFLOW SIMULATION USING DATA FUSION METHOD B. Ababaei et al.
6. Ensemble modelling of nitrogen fluxes: data fusion for a Swedish meso-scale catchment J. Exbrayat et al.
7. Dam breach parameters: from data-driven-based estimates to 2-dimensional modeling M. Azmi & K. Thomson
8. Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models J. Zaherpour et al.
9. Identification of the best multi-model combination for simulating river discharge A. Kumar et al.
10. Aproximaciones neuronales univariantes para la predicción de caudales diarios en cuencas portuguesas I. Pulido-Calvo & M. Manuela Portela
11. Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks Y. Chiang et al.
12. Multi-model data fusion as a tool for PUB: example in a Swedish mesoscale catchment J. Exbrayat et al.
13. Identifying and Interpreting Hydrological Model Structural Nonstationarity Using the Bayesian Model Averaging Method Z. Gui et al.
14. Data fabric and digital twins: An integrated approach for data fusion design and evaluation of pervasive systems A. Macías et al.
15. Multimodel Combination Techniques for Analysis of Hydrological Simulations: Application to Distributed Model Intercomparison Project Results N. Ajami et al.
16. Improving accuracy of downscaling rainfall by combining predictions of different statistical downscale models Y. Osman & M. Abdellatif
17. Improving ANN-based streamflow estimation models for the Upper Indus Basin using satellite-derived snow cover area M. Hassan & I. Hassan
18. Development and application of a planning support system to assess strategies related to land and water resources for adaptation to climate change B. Ababaei et al.
19. Methods to improve neural network performance in daily flows prediction C. Wu et al.
20. A framework for propagation of uncertainty contributed by parameterization, input data, model structure, and calibration/validation data in watershed modeling H. Yen et al.
21. Soft combination of local models in a multi-objective framework F. Fenicia et al.
22. Benchmarking hydrological models for low-flow simulation and forecasting on French catchments P. Nicolle et al.
23. Transferability of hydrological models and ensemble averaging methods between contrasting climatic periods C. Broderick et al.
24. Probabilistic load forecasting for the low voltage network: Forecast fusion and daily peaks C. Gilbert et al.
25. Improving global hydrological simulations through bias-correction and multi-model blending A. Chevuturi et al.
26. Forecasting daily reference evapotranspiration for Australia using numerical weather prediction outputs K. Perera et al.
27. Typhoon event-based evolutionary fuzzy inference model for flood stage forecasting C. Chen et al.
28. Use of Gene Expression Programming for Multimodel Combination of Rainfall-Runoff Models A. Fernando et al.
29. Baseflow separation techniques for modular artificial neural network modelling in flow forecasting G. CORZO & D. SOLOMATINE
30. Linear regressions and neural approaches to water demand forecasting in irrigation districts with telemetry systems I. Pulido-Calvo et al.
31. Improved irrigation water demand forecasting using a soft-computing hybrid model I. Pulido-Calvo & J. Gutiérrez-Estrada
32. Uncertainty quantification of CO2 storage using Bayesian model averaging and polynomial chaos expansion W. Jia et al.
33. Conceptual model uncertainty in groundwater modeling: Combining generalized likelihood uncertainty estimation and Bayesian model averaging R. Rojas et al.
34. Multimodel Approach Using Neural Networks and Symbolic Regression to Combine the Estimated Discharges of Rainfall-Runoff Models P. Phukoetphim et al.
35. Development of neuro-fuzzy models to account for temporal and spatial variations in a lumped rainfall–runoff model A. Nasr & M. Bruen
36. Load or concentration, logged or unlogged? Addressing ten years of uncertainty in neural network suspended sediment prediction N. Mount & R. Abrahart
37. Technical note: Combining quantile forecasts and predictive distributions of streamflows K. Bogner et al.
38. On the value of conditioning data to reduce conceptual model uncertainty in groundwater modeling R. Rojas et al.
39. Application of Integrated Artificial Neural Networks Based on Decomposition Methods to Predict Streamflow at Upper Indus Basin, Pakistan M. Tayyab et al.
40. A novel ensemble algorithm based on hydrological event diversity for urban rainfall–runoff model calibration and validation E. Snieder & U. Khan
41. Fuzzy neural networks for water level and discharge forecasting with uncertainty S. Alvisi & M. Franchini
42. Application of artificial neural network ensembles in probabilistic hydrological forecasting S. Araghinejad et al.
43. Heuristic Modelling of the Water Resources Management in the Guadalquivir River Basin, Southern Spain I. Pulido-Calvo et al.
44. Generating Ensemble Streamflow Forecasts: A Review of Methods and Approaches Over the Past 40 Years M. Troin et al.
45. Symbiotic adaptive neuro-evolution applied to rainfall–runoff modelling in northern England C. Dawson et al.
46. Selected model fusion: an approach for improving the accuracy of monthly streamflow forecasting F. Modaresi et al.
47. Prediction of rainfall time series using modular soft computingmethods C. Wu & K. Chau
48. Neural network modeling of hydrological systems: A review of implementation techniques O. Oyebode & D. Stretch
49. Forecasting Monthly River Flows in Ukraine under Different Climatic Conditions R. Graf & V. Vyshnevskyi
50. Real-time error correction method combined with combination flood forecasting technique for improving the accuracy of flood forecasting L. Chen et al.
51. Multivariate time series modeling of short-term system scale irrigation demand K. Perera et al.
52. Improved Convolutional Neural Network and its Application in Non-Periodical Runoff Prediction Y. Xu et al.
53. Classification of hydro-meteorological conditions and multiple artificial neural networks for streamflow forecasting E. Toth
54. Prediction and modelling of rainfall–runoff during typhoon events using a physically-based and artificial neural network hybrid model C. Young & W. Liu
55. Deep learning-based multi-model approach on electron microscopy image of renal biopsy classification J. Zhang & A. Zhang
56. Performance improvement of hydrological models using Unscented Kalman Filter-type data assimilation and data fusion P. Almasi et al.
57. A sequential Bayesian approach for hydrologic model selection and prediction K. Hsu et al.
58. Accounting for structural error and uncertainty in a model: An approach based on model parameters as stochastic processes Z. Lin & M. Beck
59. Information fusion as a tool for forecasting/prediction – An overview B. Dasarathy
60. Enhancing flood forecasting with the help of processed based calibration J. Cullmann et al.
61. A fusion-based neural network methodology for monthly reservoir inflow prediction using MODIS products M. Ghazali et al.
62. Combining Remotely Sensed Evapotranspiration and an Agroecosystem Model to Estimate Center‐Pivot Irrigation Water Use at High Spatio‐Temporal Resolution J. Zhang et al.
63. Optimizing signal decomposition techniques in artificial neural network-based rainfall-runoff model F. Karami & A. Dariane
64. Flood Forecasting using Committee Machine with Intelligent Systems: a Framework for Advanced Machine Learning Approach A. Faruq et al.
65. Real-time probabilistic forecasting of flood stages S. Chen & P. Yu
66. A wavelet based approach for combining the outputs of different rainfall–runoff models M. Shoaib et al.
67. Development of an Interdisciplinary Prediction System Combining Sediment Transport Simulation and Ensemble Method H. Ho et al.
68. Coupling a Physically Based Hydrological Model with a Modified Transformer for Long-Sequence Runoff and Peak-Flow Prediction Y. Gu et al.
69. Current awareness
70. Improving Daily Reservoir Inflow Forecasts with Model Combination P. Coulibaly et al.
71. Review of Recent Developments in Hydrologic Forecast Merging Techniques M. Sheikh & P. Coulibaly
72. Irrigation Demand Forecasting Using Artificial Neuro-Genetic Networks R. Perea et al.
73. Current awareness
74. A comparative analysis of 9 multi-model averaging approaches in hydrological continuous streamflow simulation R. Arsenault et al.
75. Combining semi-distributed process-based and data-driven models in flow simulation: a case study of the Meuse river basin G. Corzo et al.
76. River ice breakup timing prediction through stacking multi-type model trees W. Sun
77. GDROM v2: An Inventory of Operation Variables Time Series and Rules for 2,017 Large Reservoirs across the CONUS Z. Zheng et al.
78. Geomatic Techniques for the Mitigation of Hydrogeological Risk: The Modeling of Three Watercourses in Southern Italy S. Artese & G. Artese
79. Water resource management and flood mitigation: hybrid decomposition EMD-ANN model study under climate change N. Ahmad et al.
80. A hybrid bayesian vine model for water level prediction Z. Liu et al.
81. RAINFALL RUNOFF MODELLING USING NEURAL NETWORKS: STATE-OF-THE-ART AND FUTURE RESEARCH NEEDS A. Jain et al.
82. The river runoff forecast based on the modeling of time series R. Nigam et al.
83. Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting R. Abrahart et al.
84. The influence of data transformations in simulating Total Suspended Solids using Bayesian inference X. Wu et al.
85. A survey of transformer networks for time series forecasting J. Zhao et al.
86. Fuzzy committees of specialized rainfall-runoff models: further enhancements and tests N. Kayastha et al.
87. A web based tool for operational real-time flood forecasting using data assimilation to update hydraulic states M. Mure-Ravaud et al.
88. Predicting monthly streamflow using data‐driven models coupled with data‐preprocessing techniques C. Wu et al.
89. Evaluation of the impact of explanatory variables on the accuracy of prediction of daily inflow to the sewage treatment plant by selected models nonlinear B. Szeląg et al.
90. Application of neural approaches to one-step daily flow forecasting in Portuguese watersheds I. Pulido-Calvo & M. Portela
91. A stacking neuro-fuzzy framework to forecast runoff from distributed meteorological stations M. Querales et al.
92. Copula entropy coupled with artificial neural network for rainfall–runoff simulation L. Chen et al.
93. Ensemble evaluation of hydrological model hypotheses T. Krueger et al.
94. Quantitatively identifying the emission sources of pharmaceutically active compounds (PhACs) in the surface water: Method development, verification and application in Huangpu River, China X. Kan et al.
95. A stacking ensemble learning framework for annual river ice breakup dates W. Sun & B. Trevor
96. Development of a modular streamflow model to quantify runoff contributions from different land uses in tropical urban environments using Genetic Programming A. Meshgi et al.