Georgia A Papacharalampous
Georgia A Papacharalampous
PhD student, School of Civil Engineering, National Technical University of Athens
Η διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα itia.ntua.gr - Αρχική σελίδα
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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
532019
Variable Selection in Time Series Forecasting Using Random Forests
H Tyralis, G Papacharalampous
Algorithms 10 (4), 114, 2017
532017
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
51*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
392018
One-step ahead forecasting of geophysical processes within a purely statistical framework
G Papacharalampous, H Tyralis, D Koutsoyiannis
Geoscience Letters 5 (1), 12, 2018
26*2018
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
22*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
162018
Forecasting of geophysical processes using stochastic and machine learning algorithms
GA Papacharalampous, H Tyralis, D Koutsoyiannis
European Water, 161–168, 2017
162017
Evaluation of random forests and Prophet for daily streamflow forecasting
GA Papacharalampous, H Tyralis
Advances in Geosciences 45, 201–208, 2018
132018
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
112019
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
102019
Error evolution in multi-step ahead streamflow forecasting for the operation of hydropower reservoirs
G Papacharalampous, H Tyralis, D Koutsoyiannis
Preprints 2017100129, 2017
10*2017
Large-scale assessment of Prophet for multi-step ahead forecasting of monthly streamflow
H Tyralis, GA Papacharalampous
Advances in Geosciences 45, 147–153, 2018
92018
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
42020
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
42020
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
4*2019
Zone of flow establishment in turbulent jets
P Dimitriadis, M Liveri-Dalaveri, A Kaldis, C Kotsalos, ...
EGU General Assembly Conference Abstracts 14, 12716, 2012
42012
Super learning for daily streamflow forecasting: Large-scale demonstration and comparison with multiple machine learning algorithms
H Tyralis, G Papacharalampous, A Langousis
arXiv preprint arXiv:1909.04131, 2019
32019
Theoretical and empirical comparison of stochastic and machine learning methods for hydrological processes forecasting
GA Papacharalampous
National Technical University of Athens, 2017
32017
A set of metrics for the effective evaluation of point forecasting methods used for hydrological tasks
G Papacharalampous, H Tyralis, D Koutsoyiannis
AOGS 14th Annual Meeting, Singapore, 2017, HS01-A001, 2017
2*2017
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