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Hristos Tyralis
Hristos Tyralis
Hellenic Air Force
Verified email at itia.ntua.gr - Homepage
Title
Cited by
Cited by
Year
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
4242019
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
260*2019
Variable selection in time series forecasting using random forests
H Tyralis, G Papacharalampous
Algorithms 10 (4), 114, 2017
1412017
Comparison of stochastic and machine learning methods for multi-step ahead forecasting of hydrological processes
G Papacharalampous, H Tyralis, D Koutsoyiannis
Stochastic Environmental Research and Risk Assessment 33 (2), 481-514, 2019
1192019
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
1032018
Simultaneous estimation of the parameters of the Hurst–Kolmogorov stochastic process
H Tyralis, D Koutsoyiannis
Stochastic Environmental Research and Risk Assessment 25 (1), 21-33, 2011
992011
Evaluation of random forests and Prophet for daily streamflow forecasting
G Papacharalampous, H Tyralis
Advances in Geosciences 45, 201-208, 2018
63*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 33 (8), 3053-3068, 2021
612021
Hydrological post-processing using stacked generalization of quantile regression algorithms: Large-scale application over CONUS
H Tyralis, G Papacharalampous, A Burnetas, A Langousis
Journal of Hydrology 577, 123957, 2019
592019
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
552018
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
482019
On the long-range dependence properties of annual precipitation using a global network of instrumental measurements
H Tyralis, P Dimitriadis, D Koutsoyiannis, PE O'Connell, K Tzouka, ...
Advances in Water Resources 111, 301-318, 2018
422018
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
402019
One-step ahead forecasting of geophysical processes within a purely statistical framework
G Papacharalampous, H Tyralis, D Koutsoyiannis
Geoscience Letters 5 (12), 2018
40*2018
A Bayesian statistical model for deriving the predictive distribution of hydroclimatic variables
H Tyralis, D Koutsoyiannis
Climate Dynamics 42 (11-12), 2867-2883, 2014
392014
Accuracy measurement of random forests and linear regression for mass appraisal models that estimate the prices of residential apartments in Nicosia, Cyprus
T Dimopoulos, H Tyralis, NP Bakas, D Hadjimitsis
Advances in Geosciences 45, 377-382, 2018
352018
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
322020
HKprocess: Hurst-Kolmogorov process. R package version 0.1-1
H Tyralis
https://CRAN.R-project.org/package=HKprocess, 2022
31*2022
On the prediction of persistent processes using the output of deterministic models
H Tyralis, D Koutsoyiannis
Hydrological Sciences Journal 62 (13), 2083-2102, 2017
302017
Spatial analysis of the electrical energy demand in Greece
H Tyralis, N Mamassis, YN Photis
Energy Policy 102, 340-352, 2017
262017
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