150 citations · 400 across the 6 of their papers we have counts for
6 papers
Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale
Georgia Papacharalampous, Hristos Tyralis, Ilias G. Pechlivanidis +2
Statistical analyses and descriptive characterizations are sometimes assumed to be offering information on time series forecastability. Despite the scientific interest suggested by…
Global-scale massive feature extraction from monthly hydroclimatic time series: Statistical characterizations, spatial patterns and hydrological similarity
Georgia Papacharalampous, Hristos Tyralis, Simon Michael Papalexiou +4
Hydroclimatic time series analysis focuses on a few feature types (e.g., autocorrelations, trends, extremes), which describe a small portion of the entire information content of th…
Boosting algorithms in energy research: A systematic review
Hristos Tyralis, Georgia Papacharalampous
Machine learning algorithms have been extensively exploited in energy research, due to their flexibility, automation and ability to handle big data. Among the most prominent machin…
Hydrological time series forecasting using simple combinations: Big data testing and investigations on one-year ahead river flow predictability
Georgia Papacharalampous, Hristos Tyralis
Delivering useful hydrological forecasts is critical for urban and agricultural water management, hydropower generation, flood protection and management, drought mitigation and all…
Super ensemble learning for daily streamflow forecasting: Large-scale demonstration and comparison with multiple machine learning algorithms
Hristos Tyralis, Georgia Papacharalampous, Andreas Langousis
Daily streamflow forecasting through data-driven approaches is traditionally performed using a single machine learning algorithm. Existing applications are mostly restricted to exa…
Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: A large-sample experiment at monthly timescale
Georgia Papacharalampous, Hristos Tyralis, Demetris Koutsoyiannis +1
Predictive hydrological uncertainty can be quantified by using ensemble methods. If properly formulated, these methods can offer improved predictive performance by combining multip…