Georgia Papacharalampous

Publications

• Papacharalampous GA, Tyralis H, Doulamis N, Doulamis A (2024) Uncertainty estimation in spatial interpolation of satellite precipitation with ensemble learning. arxiv:2403.10567
• Papacharalampous GA, Tyralis H, Doulamis N, Doulamis A (2024) Uncertainty estimation in satellite precipitation interpolation with machine learning. arxiv:2311.07511
• Tyralis H, Papacharalampous GA, Dogulu N, Chun KP (2024) Deep Huber quantile regression networks. arXiv:2306.10306
• Tyralis H, Papacharalampous GA (2024) A review of predictive uncertainty estimation with machine learning. Artificial Intelligence Review 57:94. doi:10.1007/s10462-023-10698-8
• Papacharalampous GA, Tyralis H, Doulamis N, Doulamis A (2023) Ensemble learning for blending gridded satellite and gauge-measured precipitation data. Remote Sensing 15(20):4912. doi:10.3390/rs15204912
• Papacharalampous GA, Tyralis H, Markonis Y, Máca P, Hanel M (2023) Features of the Earth’s seasonal hydroclimate: Characterizations and comparisons across the Köppen-Geiger climates and across continents. Progress in Earth and Planetary Science 10:46. doi:10.1186/s40645-023-00574-y
• Tyralis H, Papacharalampous GA, Doulamis N, Doulamis A (2023) Merging satellite and gauge-measured precipitation using LightGBM with an emphasis on extreme quantiles. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 16:6969−6979. doi:10.1109/JSTARS.2023.3297013
• Papacharalampous GA, Tyralis H, Doulamis A, Doulamis N (2023) Comparison of tree-based ensemble algorithms for merging satellite and earth-observed precipitation data at the daily time scale. Hydrology 10(2):50. doi:10.3390/hydrology10020050
• Papacharalampous GA, Tyralis H, Doulamis A, Doulamis N (2023) Comparison of machine learning algorithms for merging gridded satellite and earth-observed precipitation data. Water 15(4):634. doi:10.3390/w15040634
• Papacharalampous GA, Tyralis H, Markonis Y, Hanel M (2023) Hydroclimatic time series features at multiple time scales. Journal of Hydrology 618:129160. doi:10.1016/j.jhydrol.2023.129160
• Tyralis H, Papacharalampous GA (2023) Hydrological post-processing for predicting extreme quantiles. Journal of Hydrology 617(Part C):129082. doi:10.1016/j.jhydrol.2023.129082
• Tyralis H, Papacharalampous GA, Khatami S (2023) Expectile-based hydrological modelling for uncertainty estimation: Life after mean. Journal of Hydrology 617(Part B):128986. doi:10.1016/j.jhydrol.2022.128986
• Papacharalampous GA, Tyralis H (2022) A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting. Frontiers in Water 4:961954. doi:10.3389/frwa.2022.961954
• Ashraf F, Tyralis H, Papacharalampous GA (2022) Explaining the flood behavior for the bridge collapse sites. Journal of Marine Science and Engineering 10(9):1241. doi:10.3390/jmse10091241
• Papacharalampous GA, Tyralis H (2022) Time series features for supporting hydrometeorological explorations and predictions in ungauged locations using large datasets. Water 14(10):1657. doi:10.3390/w14101657
• Papacharalampous GA, Langousis A (2022) Probabilistic water demand forecasting using quantile regression algorithms. Water Resources Research 58(6):e2021WR030216. doi:10.1029/2021WR030216
• Grimaldi S, Volpi E, Langousis A, Papalexiou SM, De Luca DL, Piscopia R, Nerantzaki SD, Papacharalampous GA, Petroselli A (2022) Continuous hydrologic modelling for small and ungauged basins: A comparison of eight rainfall models for sub-daily runoff simulations. Journal of Hydrology 610:127866. doi:10.1016/j.jhydrol.2022.127866
• Papacharalampous GA, Tyralis H, Pechlivanidis IG, Grimaldi S, Volpi E (2022) Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale. Geoscience Frontiers 13(3):101349. doi:10.1016/j.gsf.2022.101349
• Tyralis H, Papacharalampous GA (2021) Quantile-based hydrological modelling. Water 13(23):3420. doi:10.3390/w13233420
• Tyralis H, Papacharalampous GA (2021) Boosting algorithms in energy research: A systematic review. Neural Computing and Applications. doi:10.1007/s00521-021-05995-8
• Papacharalampous GA, Tyralis H, Papalexiou SM, Langousis A, Khatami S, Volpi E, Grimaldi S (2021) Global-scale massive feature extraction from monthly hydroclimatic time series: Statistical characterizations, spatial patterns and hydrological similarity. Science of the Total Environment 767:144612. doi:10.1016/j.scitotenv.2020.144612
• Tyralis H, Papacharalampous GA, Langousis A, Papalexiou SM (2021) Explanation and probabilistic prediction of hydrological signatures with statistical boosting algorithms. Remote Sensing 13(3):333. doi:10.3390/rs13030333
• Tyralis H, Papacharalampous GA, Langousis A (2021) Super ensemble learning for daily streamflow forecasting: Large-scale demonstration and comparison with multiple machine learning algorithms. Neural Computing and Applications 33:3053–3068. doi:10.1007/s00521-020-05172-3
• Papacharalampous GA, Tyralis H (2020) Hydrological time series forecasting using simple combinations: Big data testing and investigations on one-year ahead river flow predictability. Journal of Hydrology 590:125205. doi:10.1016/j.jhydrol.2020.125205
• Papacharalampous GA, Tyralis H, Koutsoyiannis D, Montanari A (2020) Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: A large-sample experiment at monthly timescale. Advances in Water Resources 136:103470. doi:10.1016/j.advwatres.2019.103470
• Papacharalampous GA, Koutsoyiannis D, Montanari A (2020) Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: Methodology development and investigation using toy models. Advances in Water Resources 136:103471. doi:10.1016/j.advwatres.2019.103471
• Papacharalampous GA, Tyralis H, Langousis A, Jayawardena AW, Sivakumar B, Mamassis N, Montanari A, Koutsoyiannis D (2019) Probabilistic hydrological post-processing at scale: Why and how to apply machine-learning quantile regression algorithms. Water 11(10):2126. doi:10.3390/w11102126
• Tyralis H, Papacharalampous GA, Burnetas A, Langousis A (2019) Hydrological post-processing using stacked generalization of quantile regression algorithms: Large-scale application over CONUS. Journal of Hydrology 577:123957. doi:10.1016/j.jhydrol.2019.123957
• Blöschl G, et al. (2019) Twenty-three Unsolved Problems in Hydrology (UPH) – A community perspective. Hydrological Sciences Journal 64(1):1141–1158. doi:10.1080/02626667.2019.1620507
• Tyralis H, Papacharalampous GA, Langousis A (2019) A brief review of random forests for water scientists and practitioners and their recent history in water resources. Water 11(5):910. doi:10.3390/w11050910
• Tyralis H, Papacharalampous GA, Tantanee S (2019) How to explain and predict the shape parameter of the generalized extreme value distribution of streamflow extremes using a big dataset. Journal of Hydrology 574:628–645. doi:10.1016/j.jhydrol.2019.04.070
• Papacharalampous GA, Tyralis H, Koutsoyiannis D (2019) Comparison of stochastic and machine learning methods for multi-step ahead forecasting of hydrological processes. Stochastic Environmental Research and Risk Assessment 33(2):481–514. doi:10.1007/s00477-018-1638-6
• Papacharalampous GA, Tyralis H, Koutsoyiannis D (2018) Univariate time series forecasting of temperature and precipitation with a focus on machine learning algorithms: A multiple-case study from Greece. Water Resources Management 32(15):5207–5239. doi:10.1007/s11269-018-2155-6
• Papacharalampous GA, Tyralis H (2018) Evaluation of random forests and Prophet for daily streamflow forecasting. Advances in Geosciences 45:201–208. doi:10.5194/adgeo-45-201-2018
• Tyralis H, Papacharalampous GA (2018) Large-scale assessment of Prophet for multi-step ahead forecasting of monthly streamflow. Advances in Geosciences 45:147–153. doi:10.5194/adgeo-45-147-2018
• Papacharalampous GA, Tyralis H, Koutsoyiannis D (2018) Predictability of monthly temperature and precipitation using automatic time series forecasting methods. Acta Geophysica 66(4):807–831. doi:10.1007/s11600-018-0120-7
• Papacharalampous GA, Tyralis H, Koutsoyiannis D (2018) One-step ahead forecasting of geophysical processes within a purely statistical framework. Geoscience Letters 5(1):12. doi:10.1186/s40562-018-0111-1
• Tyralis H, Papacharalampous GA (2017) Variable selection in time series forecasting using random forests. Algorithms 10(4):114. doi:10.3390/a10040114
• Papacharalampous GA, Tyralis H, Koutsoyiannis D (2017) Forecasting of geophysical processes using stochastic and machine learning algorithms. European Water 59:161–168