Hristos Tyralis

According to our database, Hristos Tyralis authored at least 19 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2024

Uncertainty estimation in satellite precipitation spatial prediction by combining distributional regression algorithms.

Georgia Papacharalampous, Hristos Tyralis, Nikolaos Doulamis, Anastasios Doulamis

CoRR, 2024

Uncertainty estimation in spatial interpolation of satellite precipitation with ensemble learning.

Georgia Papacharalampous, Hristos Tyralis, Nikolaos Doulamis, Anastasios Doulamis

CoRR, 2024

A review of predictive uncertainty estimation with machine learning.

Hristos Tyralis, Georgia Papacharalampous

Artif. Intell. Rev., 2024

2023

Ensemble Learning for Blending Gridded Satellite and Gauge-Measured Precipitation Data.

Georgia Papacharalampous, Hristos Tyralis, Nikolaos Doulamis, Anastasios Doulamis

Remote. Sens., October, 2023

Merging Satellite and Gauge-Measured Precipitation Using LightGBM With an Emphasis on Extreme Quantiles.

Hristos Tyralis, Georgia Papacharalampous, Nikolaos Doulamis, Anastasios D. Doulamis

IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2023

Machine learning for uncertainty estimation in fusing precipitation observations from satellites and ground-based gauges.

Georgia Papacharalampous, Hristos Tyralis, Nikolaos Doulamis, Anastasios Doulamis

CoRR, 2023

Deep Huber quantile regression networks.

Hristos Tyralis, Georgia Papacharalampous, Nilay Dogulu, Kwok P. Chun

CoRR, 2023

Comparison of machine learning algorithms for merging gridded satellite and earth-observed precipitation data.

Georgia Papacharalampous, Hristos Tyralis, Anastasios D. Doulamis, Nikolaos Doulamis

CoRR, 2023

Comparison of tree-based ensemble algorithms for merging satellite and earth-observed precipitation data at the daily time scale.

Georgia Papacharalampous, Hristos Tyralis, Anastasios D. Doulamis, Nikolaos Doulamis

CoRR, 2023

2022

A review of probabilistic forecasting and prediction with machine learning.

Hristos Tyralis, Georgia Papacharalampous

CoRR, 2022

A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting.

Georgia Papacharalampous, Hristos Tyralis

CoRR, 2022

2021

Explanation and Probabilistic Prediction of Hydrological Signatures with Statistical Boosting Algorithms.

Hristos Tyralis, Georgia Papacharalampous, Andreas Langousis, Simon Michael Papalexiou

Remote. Sens., 2021

Super ensemble learning for daily streamflow forecasting: large-scale demonstration and comparison with multiple machine learning algorithms.

Hristos Tyralis, Georgia Papacharalampous, Andreas Langousis

Neural Comput. Appl., 2021

Boosting algorithms in energy research: a systematic review.

Hristos Tyralis, Georgia Papacharalampous

Neural Comput. Appl., 2021

Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale.

Georgia Papacharalampous, Hristos Tyralis, Ilias G. Pechlivanidis, Salvatore Grimaldi, Elena Volpi

CoRR, 2021

2020

Hydrological time series forecasting using simple combinations: Big data testing and investigations on one-year ahead river flow predictability.

Georgia Papacharalampous, Hristos Tyralis

CoRR, 2020

2019

Super learning for daily streamflow forecasting: Large-scale demonstration and comparison with multiple machine learning algorithms.

Hristos Tyralis, Georgia Papacharalampous, Andreas Langousis

CoRR, 2019

2017

Variable Selection in Time Series Forecasting Using Random Forests.

Hristos Tyralis, Georgia Papacharalampous

Algorithms, 2017

2013

An algorithm to construct Monte Carlo confidence intervals for an arbitrary function of probability distribution parameters.

Hristos Tyralis, Demetris Koutsoyiannis, Stefanos Kozanis

Comput. Stat., 2013