| 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 | 648 | 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 | 458* | 2019 |
| Variable Selection in Time Series Forecasting Using Random Forests H Tyralis, G Papacharalampous Algorithms 10 (4), 114, 2017 | 183 | 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 | 148 | 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 | 133 | 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 | 126 | 2020 |
| Evaluation of random forests and Prophet for daily streamflow forecasting GA Papacharalampous, H Tyralis Advances in Geosciences 45, 201–208, 2018 | 89* | 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 | 73 | 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 | 58 | 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 | 45 | 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 | 39 | 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 | 37 | 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 | 32 | 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 | 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 |
| 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 | 23 | 2022 |
Hristos TyralisHellenic Air ForceVerified email at itia.ntua.gr
