Twenty-three Unsolved Problems in Hydrology (UPH)–a community perspective G Blöschl, MFP Bierkens, A Chambel, C Cudennec, G Destouni, A Fiori, ... Hydrological Sciences Journal 64 (10), 1141–1158, 2019 | 694 | 2019 |
A brief review of random forests for water scientists and practitioners and their recent history in water resources H Tyralis, G Papacharalampous, A Langousis Water 11 (5), 910, 2019 | 481* | 2019 |
Variable Selection in Time Series Forecasting Using Random Forests H Tyralis, G Papacharalampous Algorithms 10 (4), 114, 2017 | 194 | 2017 |
Comparison of stochastic and machine learning methods for multi-step ahead forecasting of hydrological processes GA Papacharalampous, H Tyralis, D Koutsoyiannis Stochastic Environmental Research and Risk Assessment 33 (2), 481–514, 2019 | 150 | 2019 |
Predictability of monthly temperature and precipitation using automatic time series forecasting methods G Papacharalampous, H Tyralis, D Koutsoyiannis Acta Geophysica 66 (4), 807–831, 2018 | 136 | 2018 |
Super ensemble learning for daily streamflow forecasting: Large-scale demonstration and comparison with multiple machine learning algorithms H Tyralis, G Papacharalampous, A Langousis Neural Computing and Applications, 1-16, 2020 | 129 | 2020 |
Evaluation of random forests and Prophet for daily streamflow forecasting GA Papacharalampous, H Tyralis Advances in Geosciences 45, 201–208, 2018 | 90* | 2018 |
Hydrological post-processing using stacked generalization of quantile regression algorithms: Large-scale application over CONUS H Tyralis, GA Papacharalampous, A Burnetas, A Langousis Journal of Hydrology 577, 123957, 2019 | 83 | 2019 |
Univariate time series forecasting of temperature and precipitation with a focus on machine learning algorithms: A multiple-case study from Greece G Papacharalampous, H Tyralis, D Koutsoyiannis Water Resources Management 32 (15), 5207–5239, 2018 | 74 | 2018 |
Probabilistic hydrological post-processing at scale: Why and how to apply machine-learning quantile regression algorithms G Papacharalampous, H Tyralis, A Langousis, AW Jayawardena, ... Water 11 (10), 2126, 2019 | 59 | 2019 |
How to explain and predict the shape parameter of the generalized extreme value distribution of streamflow extremes using a big dataset H Tyralis, G Papacharalampous, S Tantanee Journal of Hydrology 574, 628–645, 2019 | 51 | 2019 |
Boosting algorithms in energy research: A systematic review H Tyralis, G Papacharalampous Neural Computing and Applications 33 (21), 14101-14117, 2021 | 46 | 2021 |
One-step ahead forecasting of geophysical processes within a purely statistical framework G Papacharalampous, H Tyralis, D Koutsoyiannis Geoscience Letters 5 (1), 12, 2018 | 44* | 2018 |
Global-scale massive feature extraction from monthly hydroclimatic time series: Statistical characterizations, spatial patterns and hydrological similarity G Papacharalampous, H Tyralis, SM Papalexiou, A Langousis, S Khatami, ... Science of The Total Environment 767, 144612, 2021 | 40 | 2021 |
Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: A large-sample experiment at monthly timescale G Papacharalampous, H Tyralis, D Koutsoyiannis, A Montanari Advances in Water Resources 136, 103470, 2020 | 38 | 2020 |
Hydrological time series forecasting using simple combinations: Big data testing and investigations on one-year ahead river flow predictability G Papacharalampous, H Tyralis Journal of Hydrology 590, 125205, 2020 | 36 | 2020 |
Large-scale assessment of Prophet for multi-step ahead forecasting of monthly streamflow H Tyralis, GA Papacharalampous Advances in Geosciences 45, 147–153, 2018 | 33 | 2018 |
A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting G Papacharalampous, H Tyralis Frontiers in Water 4, 961954, 2022 | 27* | 2022 |
Continuous hydrologic modelling for small and ungauged basins: A comparison of eight rainfall models for sub-daily runoff simulations S Grimaldi, E Volpi, A Langousis, SM Papalexiou, DL De Luca, ... Journal of Hydrology 610, 127866, 2022 | 26 | 2022 |
Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: Methodology development and investigation using toy models G Papacharalampous, D Koutsoyiannis, A Montanari Advances in Water Resources 136, 103471, 2020 | 24 | 2020 |