53 citations as recorded by crossref.

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8. Uncertainty Estimation Using the Glue and Bayesian Approaches in Flood Estimation: A case Study—Ba River, Vietnam P. Cu Thi et al. 10.3390/w10111641
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11. Impact of Dataset Size on the Signature-Based Calibration of a Hydrological Model S. Mohammed et al. 10.3390/w13070970
12. Comparison of Tree-Based Ensemble Algorithms for Merging Satellite and Earth-Observed Precipitation Data at the Daily Time Scale G. Papacharalampous et al. 10.3390/hydrology10020050
13. Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNEEC methods O. Rahmati et al. 10.1016/j.scitotenv.2019.06.320
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22. The risk assessment of arsenic contamination in the urbanized coastal aquifer of Rayong groundwater basin, Thailand using the machine learning approach N. Sumdang et al. 10.1016/j.ecoenv.2023.114665
23. Probabilistic Water Demand Forecasting Using Quantile Regression Algorithms G. Papacharalampous & A. Langousis 10.1029/2021WR030216
24. A Framework for Recalibrating Pedotransfer Functions Using Nonlinear Least Squares and Estimating Uncertainty Using Quantile Regression B. Heung et al. 10.2139/ssrn.4352027
25. Post-Processing of Stream Flows in Switzerland with an Emphasis on Low Flows and Floods K. Bogner et al. 10.3390/w8040115
26. Quantifying uncertainty in future sea level projections downscaled from CMIP5 global climate models S. Sithara et al. 10.1007/s00477-024-02669-4
27. Performance Assessment of Hydrological Models Considering Acceptable Forecast Error Threshold Q. Dong & F. Lu 10.3390/w7116173
28. Dynamic quantile regression for trend analysis of streamflow time series L. Lima et al. 10.1002/rra.3983
29. Spatial mode-based calibration (SMoC) of forecast precipitation fields from numerical weather prediction models P. Zhao et al. 10.1016/j.jhydrol.2022.128432
30. Ensemble Learning for Blending Gridded Satellite and Gauge-Measured Precipitation Data G. Papacharalampous et al. 10.3390/rs15204912
31. Hydrological post-processing using stacked generalization of quantile regression algorithms: Large-scale application over CONUS H. Tyralis et al. 10.1016/j.jhydrol.2019.123957
32. Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system S. Sharma et al. 10.5194/hess-22-1831-2018
33. Assessing future rainfall uncertainties of climate change in Taiwan with a bootstrapped neural network‐based downscaling model C. Li et al. 10.1111/wej.12443
34. Hydrological post-processing for predicting extreme quantiles H. Tyralis & G. Papacharalampous 10.1016/j.jhydrol.2023.129082
35. A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting G. Papacharalampous & H. Tyralis 10.3389/frwa.2022.961954
36. Nation-wide estimation of groundwater redox conditions and nitrate concentrations through machine learning L. Knoll et al. 10.1088/1748-9326/ab7d5c
37. Extending a joint probability modelling approach for post-processing ensemble precipitation forecasts from numerical weather prediction models P. Zhao et al. 10.1016/j.jhydrol.2021.127285
38. Comparison of different quantile regression methods to estimate predictive hydrological uncertainty in the Upper Chao Phraya River Basin, Thailand S. Acharya et al. 10.1111/jfr3.12585
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41. Adjusting for Conditional Bias in Process Model Simulations of Hydrological Extremes: An Experiment Using the North Wyke Farm Platform S. Curceac et al. 10.3389/frai.2020.565859
42. Comparative Study of Two State-of-the-Art Semi-Distributed Hydrological Models P. Paul et al. 10.3390/w11050871
43. Bayesian LSTM With Stochastic Variational Inference for Estimating Model Uncertainty in Process‐Based Hydrological Models D. Li et al. 10.1029/2021WR029772
44. Explainable deeply-fused nets electricity demand prediction model: Factoring climate predictors for accuracy and deeper insights with probabilistic confidence interval and point-based forecasts S. Ghimire et al. 10.1016/j.apenergy.2024.124763
45. Probabilistic Hydrological Post-Processing at Scale: Why and How to Apply Machine-Learning Quantile Regression Algorithms G. Papacharalampous et al. 10.3390/w11102126
46. Skill of Hydrological Extended Range Forecasts for Water Resources Management in Switzerland K. Bogner et al. 10.1007/s11269-017-1849-5
47. Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: A large-sample experiment at monthly timescale G. Papacharalampous et al. 10.1016/j.advwatres.2019.103470
48. Diagnosing Credibility of a Large-Scale Conceptual Hydrological Model in Simulating Streamflow P. Paul et al. 10.1061/(ASCE)HE.1943-5584.0001766
49. A crash-testing framework for predictive uncertainty assessment when forecasting high flows in an extrapolation context L. Berthet et al. 10.5194/hess-24-2017-2020
50. Changes in extreme rainfall and its implications for design rainfall using a Bayesian quantile regression approach S. Uranchimeg et al. 10.2166/nh.2020.003
51. A review on statistical postprocessing methods for hydrometeorological ensemble forecasting W. Li et al. 10.1002/wat2.1246
52. Characterizing distributed hydrological model residual errors using a probabilistic long short-term memory network D. Li et al. 10.1016/j.jhydrol.2021.126888
53. Alternative configurations of quantile regression for estimating predictive uncertainty in water level forecasts for the upper Severn River: a comparison P. López López et al. 10.5194/hess-18-3411-2014