41 citations as recorded by crossref.

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3. A hybrid model for river water level forecasting: Cases of Xiangjiang River and Yuanjiang River, China Y. Li et al. https://doi.org/10.1016/j.jhydrol.2020.124934
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7. The Potential of Knowing More: A Review of Data-Driven Urban Water Management S. Eggimann et al. https://doi.org/10.1021/acs.est.6b04267
8. Streamflow forecasts from WRF precipitation for flood early warning in mountain tropical areas M. Rogelis & M. Werner https://doi.org/10.5194/hess-22-853-2018
9. Epistemic uncertainties and natural hazard risk assessment – Part 1: A review of different natural hazard areas K. Beven et al. https://doi.org/10.5194/nhess-18-2741-2018
10. Adjusting for Conditional Bias in Process Model Simulations of Hydrological Extremes: An Experiment Using the North Wyke Farm Platform S. Curceac et al. https://doi.org/10.3389/frai.2020.565859
11. A review on statistical postprocessing methods for hydrometeorological ensemble forecasting W. Li et al. https://doi.org/10.1002/wat2.1246
12. Comparison of Different Configurations of Quantile Regression in Estimating Predictive Hydrological Uncertainty M. Muthusamy et al. https://doi.org/10.1016/j.proeng.2016.07.546
13. Quantile-Based Hydrological Modelling H. Tyralis & G. Papacharalampous https://doi.org/10.3390/w13233420
14. Requirements for Flood-Driven Forecasting Systems for Small and Medium-Sized Catchments in Germany J. Leandro et al. https://doi.org/10.3390/w17223283
15. Skill of ensemble flood inundation forecasts at short- to medium-range timescales M. Gomez et al. https://doi.org/10.1016/j.jhydrol.2018.10.063
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19. Residual uncertainty estimation using instance-based learning with applications to hydrologic forecasting O. Wani et al. https://doi.org/10.5194/hess-21-4021-2017
20. Comparison of different quantile regression methods to estimate predictive hydrological uncertainty in the Upper Chao Phraya River Basin, Thailand S. Acharya et al. https://doi.org/10.1111/jfr3.12585
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29. Filling the gaps in soil data: A multi-model framework for addressing data gaps using pedotransfer functions and machine-learning with uncertainty estimates to estimate bulk density A. Arbor et al. https://doi.org/10.1016/j.catena.2024.108310
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35. Hydrological post-processing using stacked generalization of quantile regression algorithms: Large-scale application over CONUS H. Tyralis et al. https://doi.org/10.1016/j.jhydrol.2019.123957
36. Design of data-driven model for the pressurizer system in nuclear power plants using a TSK fuzzy neural network T. Mahmoud et al. https://doi.org/10.1016/j.nucengdes.2022.112015
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38. Runoff Probability Prediction Model Based on Natural Gradient Boosting with Tree-Structured Parzen Estimator Optimization K. Shen et al. https://doi.org/10.3390/w14040545
39. Time series predictions in unmonitored sites: a survey of machine learning techniques in water resources J. Willard et al. https://doi.org/10.1017/eds.2024.14
40. Machine learning approaches to predict significant wave height and assessment of model uncertainty S. Sithara et al. https://doi.org/10.1016/j.oceaneng.2025.121039
41. Predictive Uncertainty Estimation of Hydrological Multi-Model Ensembles Using Pair-Copula Construction B. Klein et al. https://doi.org/10.3390/w8040125