Hristos Tyralis

Cited by

	All	Since 2018
Citations	2320	2159
h-index	24	23
i10-index	36	32

620

310

155

465

20142015201620172018201920202021202220239 12 18 99 127 225 428 544 603 226

Public access

View all

5 articles

0 articles

available

not available

Based on funding mandates

Co-authors

Georgia A PapacharalampousResearcher at the National Technical University of AthensVerified email at hydro.ntua.gr
Demetris KoutsoyiannisProf. Em., Hydrology & Hydrosystems; Dept. Water Resources; School of Civil Engineering; NTU AthensVerified email at itia.ntua.gr
Andreas LangousisAssociate Professor, Department of Civil Engineering, University of Patras (UPatras), GreeceVerified email at upatras.gr
Nikos MamassisProfessor of Hydrology; Dept. of Water Resources; School of Civil Engineering; NTU AthensVerified email at itia.ntua.gr
Panayiotis DimitriadisDepartment of Water Resources and Environmental Engineering, School of Civil Engineering, NTUAVerified email at itia.ntua.gr
Theano IliopoulouNational Technical University of Athens, School of Civil Engineering, Department of Water ResourcesVerified email at central.ntua.gr
Alberto MontanariProfessor of Hydrology and Water Engineering, University of BolognaVerified email at unibo.it
Georgios KarakatsanisPhD Researcher in Energy, environment and economic growth, National Technical University of AthensVerified email at itia.ntua.gr
Simon Michael PapalexiouUniversity of Calgary, CanadaVerified email at ucalgary.ca
Apostolos BurnetasDepartment of Mathematics, National and Kapodistrian University of AthensVerified email at math.uoa.gr
Aristoteles TegosNational Technical University AthensVerified email at itia.ntua.gr
Bellie SivakumarIndian Institute of Technology BombayVerified email at iitb.ac.in
Elena VolpiUniversità Roma TreVerified email at uniroma3.it
Salvatore GRIMALDIProfessor of Hydrology, Università degli Studi della Tuscia, Viterbo, ItalyVerified email at unitus.it
Yorgos N. PhotisProfessor of Geographical Analysis and Locational Planning, National Technical University of AthensVerified email at mail.ntua.gr
Yannis MarkonisCzech University of Life Sciences PragueVerified email at fzp.czu.cz
Sina KhatamiUppsala UniversityVerified email at geo.uu.se
Andreas EfstratiadisAssistant Professor, National Technical University of AthensVerified email at itia.ntua.gr
Sarintip TantaneeNaresuan UniversityVerified email at nu.ac.th
Thomas DimopoulosNeapolis University PafosVerified email at nup.ac.cy

Hristos Tyralis

Hellenic Air Force

Verified email at itia.ntua.gr - Homepage

hydrology probabilistic forecasting random forests machine learning statistical learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
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	477	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	306*	2019
Variable selection in time series forecasting using random forests H Tyralis, G Papacharalampous Algorithms 10 (4), 114, 2017	146	2017
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	122	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	117	2018
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	98	2011
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	77	2021
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	70	2019
Evaluation of random forests and Prophet for daily streamflow forecasting G Papacharalampous, H Tyralis Advances in Geosciences 45, 201-208, 2018	69*	2018
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	60	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	52	2019
A Bayesian statistical model for deriving the predictive distribution of hydroclimatic variables H Tyralis, D Koutsoyiannis Climate Dynamics 42 (11-12), 2867-2883, 2014	43	2014
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	42	2018
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	41	2019
One-step ahead forecasting of geophysical processes within a purely statistical framework G Papacharalampous, H Tyralis, D Koutsoyiannis Geoscience Letters 5 (12), 2018	41*	2018
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	39	2018
HKprocess: Hurst-Kolmogorov process. R package version 0.1-1 H Tyralis https://CRAN.R-project.org/package=HKprocess, 2022	35*	2022
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	34	2020
Boosting algorithms in energy research: A systematic review H Tyralis, G Papacharalampous Neural Computing and Applications 33 (21), 14101-14117, 2021	31	2021
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	29	2020

The system can't perform the operation now. Try again later.

Articles 1–20

Citations per year

Duplicate citations

Merged citations

Add co-authorsCo-authors

Follow

Cited by

Co-authors