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20212026
most citedA review of predictive uncertainty estimation with machine learning

158 citations · 341 across the 22 of their papers we have counts for

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6 papers · 1 filter

stat.ML2022★ 158 cited

A review of predictive uncertainty estimation with machine learning

Hristos Tyralis, Georgia Papacharalampous

Predictions and forecasts of machine learning models should take the form of probability distributions, aiming to increase the quantity of information communicated to end users. Al…

cs.LG2022★ 4 cited

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

Georgia Papacharalampous, Hristos Tyralis

Probabilistic forecasting is receiving growing attention nowadays in a variety of applied fields, including hydrology. Several machine learning concepts and methods are notably rel…

stat.AP2022★ 8 cited

Features of the Earth's seasonal hydroclimate: Characterizations and comparisons across the Koppen-Geiger climates and across continents

Georgia Papacharalampous, Hristos Tyralis, Yannis Markonis +2

Detailed investigations of time series features across climates, continents and variable types can progress our understanding and modelling ability of the Earth's hydroclimate and…

stat.ME2022

Time series features for supporting hydrometeorological explorations and predictions in ungauged locations using large datasets

Georgia Papacharalampous, Hristos Tyralis

Regression-based frameworks for streamflow regionalization are built around catchment attributes that traditionally originate from catchment hydrology, flood frequency analysis and…

stat.ME2022★ 19 cited

Hydrological post-processing for predicting extreme quantiles

Hristos Tyralis, Georgia Papacharalampous

Hydrological post-processing using quantile regression algorithms constitutes a prime means of estimating the uncertainty of hydrological predictions. Nonetheless, conventional lar…

stat.AP2022★ 22 cited

Expectile-based hydrological modelling for uncertainty estimation: Life after mean

Hristos Tyralis, Georgia Papacharalampous, Sina Khatami

Predictions of hydrological models should be probabilistic in nature. Our aim is to introduce a method that estimates directly the uncertainty of hydrological simulations using exp…