158 citations · 341 across the 22 of their papers we have counts for
22 papers
Kling-Gupta linear regression
Hristos Tyralis, Georgia Papacharalampous
Kling-Gupta efficiency () is a model performance evaluation metric widely used in hydrology, but its properties as a statistical estimator have remained unexplored. W…
Learning with the Nash-Sutcliffe loss
Hristos Tyralis, Georgia Papacharalampous
The Nash-Sutcliffe efficiency () is a widely used, positively oriented relative measure for evaluating forecasts across multiple time series. However, it lacks a decisi…
Loss functions arising from the index of agreement
Hristos Tyralis, Georgia Papacharalampous
We examine the theoretical properties of the index of agreement loss function , the negatively oriented counterpart of Willmott's index of agreement, a common metric in enviro…
Variable transformations in consistent loss functions
Hristos Tyralis, Georgia Papacharalampous
The empirical use of variable transformations within (strictly) consistent loss functions is widespread, yet a theoretical understanding is lacking. To address this gap, we develop…
Transforming disaster risk reduction with AI and big data: Legal and interdisciplinary perspectives
Kwok P Chun, Thanti Octavianti, Nilay Dogulu +24
Managing complex disaster risks requires interdisciplinary efforts. Breaking down silos between law, social sciences, and natural sciences is critical for all processes of disaster…
Unravelling compound risks of hydrological extremes in a changing climate: Typology, methods and futures
Kwok P Chun, Thanti Octavianti, Georgia Papacharalampous +21
We have witnessed and experienced increasing compound extreme events resulting from simultaneous or sequential occurrence of multiple events in a changing climate. In addition to a…
Combinations of distributional regression algorithms with application in uncertainty estimation of corrected satellite precipitation products
Georgia Papacharalampous, Hristos Tyralis, Nikolaos Doulamis +1
To facilitate effective decision-making, precipitation datasets should include uncertainty estimates. Quantile regression with machine learning has been proposed for issuing such e…
Ensemble learning for uncertainty estimation with application to the correction of satellite precipitation products
Georgia Papacharalampous, Hristos Tyralis, Nikolaos Doulamis +1
Predictions in the form of probability distributions are crucial for effective decision-making. Quantile regression enables such predictions within spatial prediction settings that…
Uncertainty estimation of machine learning spatial precipitation predictions from satellite data
Georgia Papacharalampous, Hristos Tyralis, Nikolaos Doulamis +1
Merging satellite and gauge data with machine learning produces high-resolution precipitation datasets, but uncertainty estimates are often missing. We addressed the gap of how to…
Ensemble learning for blending gridded satellite and gauge-measured precipitation data
Georgia Papacharalampous, Hristos Tyralis, Nikolaos Doulamis +1
Regression algorithms are regularly used for improving the accuracy of satellite precipitation products. In this context, satellite precipitation and topography data are the predic…
Deep Huber quantile regression networks
Hristos Tyralis, Georgia Papacharalampous, Nilay Dogulu +1
Typical machine learning regression applications aim to report the mean or the median of the predictive probability distribution, via training with a squared or an absolute error s…
Comparison of machine learning algorithms for merging gridded satellite and earth-observed precipitation data
Georgia Papacharalampous, Hristos Tyralis, Anastasios Doulamis +1
Gridded satellite precipitation datasets are useful in hydrological applications as they cover large regions with high density. However, they are not accurate in the sense that the…
Comparison of tree-based ensemble algorithms for merging satellite and earth-observed precipitation data at the daily time scale
Georgia Papacharalampous, Hristos Tyralis, Anastasios Doulamis +1
Merging satellite products and ground-based measurements is often required for obtaining precipitation datasets that simultaneously cover large regions with high density and are mo…
Merging satellite and gauge-measured precipitation using LightGBM with an emphasis on extreme quantiles
Hristos Tyralis, Georgia Papacharalampous, Nikolaos Doulamis +1
Knowing the actual precipitation in space and time is critical in hydrological modelling applications, yet the spatial coverage with rain gauge stations is limited due to economic…
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…
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…
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…
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…
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…
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…
Hydroclimatic time series features at multiple time scales
Georgia Papacharalampous, Hristos Tyralis, Yannis Markonis +1
A comprehensive understanding of the behaviours of the various geophysical processes and an effective evaluation of time series (else referred to as "stochastic") simulation models…
Quantile-based hydrological modelling
Hristos Tyralis, Georgia Papacharalampous
Predictive uncertainty in hydrological modelling is quantified by using post-processing or Bayesian-based methods. The former methods are not straightforward and the latter ones ar…