34 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. 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. 10.1021/acs.est.6b04267
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10. 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
11. A review on statistical postprocessing methods for hydrometeorological ensemble forecasting W. Li et al. 10.1002/wat2.1246
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16. 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
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19. Probabilistic Hydrological Post-Processing at Scale: Why and How to Apply Machine-Learning Quantile Regression Algorithms G. Papacharalampous et al. 10.3390/w11102126
20. 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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24. Performance and robustness of probabilistic river forecasts computed with quantile regression based on multiple independent variables F. Hoss & P. Fischbeck 10.5194/hess-19-3969-2015
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27. Post-Processing of Stream Flows in Switzerland with an Emphasis on Low Flows and Floods K. Bogner et al. 10.3390/w8040115
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30. Design of data-driven model for the pressurizer system in nuclear power plants using a TSK fuzzy neural network T. Mahmoud et al. 10.1016/j.nucengdes.2022.112015
31. Local vs. integrated control of a variable refrigerant flow system using artificial neural networks K. Ahn et al. 10.1080/23744731.2020.1760636
32. Runoff Probability Prediction Model Based on Natural Gradient Boosting with Tree-Structured Parzen Estimator Optimization K. Shen et al. 10.3390/w14040545
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