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ORCIDGeorgia Papacharalampous
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2020 – today
- 2024
[j7]Hristos Tyralis
, Georgia Papacharalampous:
A review of predictive uncertainty estimation with machine learning. Artif. Intell. Rev. 57(4): 94 (2024)- [i15]Georgia Papacharalampous, Hristos Tyralis, Nikolaos Doulamis, Anastasios Doulamis:
Uncertainty estimation in spatial interpolation of satellite precipitation with ensemble learning. CoRR abs/2403.10567 (2024) - [i14]Georgia Papacharalampous, Hristos Tyralis, Nikolaos Doulamis, Anastasios Doulamis:
Uncertainty estimation in satellite precipitation spatial prediction by combining distributional regression algorithms. CoRR abs/2407.01623 (2024) - 2023
- [j6]Georgia Papacharalampous, Hristos Tyralis, Nikolaos Doulamis, Anastasios Doulamis:
Ensemble Learning for Blending Gridded Satellite and Gauge-Measured Precipitation Data. Remote. Sens. 15(20): 4912 (2023) - [j5]Hristos Tyralis, Georgia Papacharalampous, Nikolaos Doulamis, Anastasios D. Doulamis:
Merging Satellite and Gauge-Measured Precipitation Using LightGBM With an Emphasis on Extreme Quantiles. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 16: 6969-6979 (2023) - [i13]Georgia Papacharalampous, Hristos Tyralis, Anastasios D. Doulamis, Nikolaos Doulamis:
Comparison of tree-based ensemble algorithms for merging satellite and earth-observed precipitation data at the daily time scale. CoRR abs/2301.01214 (2023) - [i12]Georgia Papacharalampous, Hristos Tyralis, Anastasios D. Doulamis, Nikolaos Doulamis:
Comparison of machine learning algorithms for merging gridded satellite and earth-observed precipitation data. CoRR abs/2301.01252 (2023) - [i11]Hristos Tyralis, Georgia Papacharalampous, Nikolaos D. Doulamis, Anastasios D. Doulamis:
Merging satellite and gauge-measured precipitation using LightGBM with an emphasis on extreme quantiles. CoRR abs/2302.03606 (2023) - [i10]Hristos Tyralis, Georgia Papacharalampous, Nilay Dogulu, Kwok P. Chun:
Deep Huber quantile regression networks. CoRR abs/2306.10306 (2023) - [i9]Georgia Papacharalampous, Hristos Tyralis, Nikolaos D. Doulamis, Anastasios D. Doulamis:
Ensemble learning for blending gridded satellite and gauge-measured precipitation data. CoRR abs/2307.06840 (2023) - [i8]Georgia Papacharalampous, Hristos Tyralis, Nikolaos Doulamis, Anastasios Doulamis:
Machine learning for uncertainty estimation in fusing precipitation observations from satellites and ground-based gauges. CoRR abs/2311.07511 (2023) - 2022
- [i7]Georgia Papacharalampous, Hristos Tyralis:
A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting. CoRR abs/2206.08998 (2022) - [i6]Hristos Tyralis, Georgia Papacharalampous:
A review of probabilistic forecasting and prediction with machine learning. CoRR abs/2209.08307 (2022) - 2021
- [j4]Hristos Tyralis, Georgia Papacharalampous, Andreas Langousis:
Super ensemble learning for daily streamflow forecasting: large-scale demonstration and comparison with multiple machine learning algorithms. Neural Comput. Appl. 33(8): 3053-3068 (2021) - [j3]Hristos Tyralis, Georgia Papacharalampous:
Boosting algorithms in energy research: a systematic review. Neural Comput. Appl. 33(21): 14101-14117 (2021) - [j2]Hristos Tyralis, Georgia Papacharalampous, Andreas Langousis, Simon Michael Papalexiou:
Explanation and Probabilistic Prediction of Hydrological Signatures with Statistical Boosting Algorithms. Remote. Sens. 13(3): 333 (2021) - [i5]Georgia Papacharalampous, Andreas Langousis:
Probabilistic water demand forecasting using quantile regression algorithms. CoRR abs/2104.07985 (2021) - [i4]Georgia Papacharalampous, Hristos Tyralis, Ilias G. Pechlivanidis, Salvatore Grimaldi, Elena Volpi:
Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale. CoRR abs/2108.00846 (2021) - 2020
- [b1]Georgia Papacharalampous:
Stochastic process-based modelling for hydrological systems. National Technical University of Athens, Greece, 2020 - [i3]Georgia Papacharalampous, Hristos Tyralis:
Hydrological time series forecasting using simple combinations: Big data testing and investigations on one-year ahead river flow predictability. CoRR abs/2001.00811 (2020) - [i2]Hristos Tyralis, Georgia Papacharalampous:
Boosting algorithms in energy research: A systematic review. CoRR abs/2004.07049 (2020)
2010 – 2019
- 2019
- [i1]Hristos Tyralis, Georgia Papacharalampous, Andreas Langousis:
Super learning for daily streamflow forecasting: Large-scale demonstration and comparison with multiple machine learning algorithms. CoRR abs/1909.04131 (2019) - 2017
- [j1]Hristos Tyralis, Georgia Papacharalampous:
Variable Selection in Time Series Forecasting Using Random Forests. Algorithms 10(4): 114 (2017)
Coauthor Index
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