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Georgia A Papacharalampous
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Twenty-three Unsolved Problems in Hydrology (UPH)–a community perspective
G Bl÷schl, MFP Bierkens, A Chambel, C Cudennec, G Destouni, A Fiori, ...
Hydrological Sciences Journal 64 (10), 1141–1158, 2019
6182019
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
440*2019
Variable Selection in Time Series Forecasting Using Random Forests
H Tyralis, G Papacharalampous
Algorithms 10 (4), 114, 2017
1712017
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
1412019
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
1322018
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
1152020
Evaluation of random forests and Prophet for daily streamflow forecasting
GA Papacharalampous, H Tyralis
Advances in Geosciences 45, 201–208, 2018
88*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
802019
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
692018
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
562019
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
472019
Boosting algorithms in energy research: A systematic review
H Tyralis, G Papacharalampous
Neural Computing and Applications 33 (21), 14101-14117, 2021
432021
One-step ahead forecasting of geophysical processes within a purely statistical framework
G Papacharalampous, H Tyralis, D Koutsoyiannis
Geoscience Letters 5 (1), 12, 2018
43*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
372021
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
372020
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
352020
Large-scale assessment of Prophet for multi-step ahead forecasting of monthly streamflow
H Tyralis, GA Papacharalampous
Advances in Geosciences 45, 147–153, 2018
312018
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
232020
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
222022
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
21*2022
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