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

v1v2 (latest)

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

25 July 2021

Georgia Papacharalampous

Hristos Tyralis

I. Pechlivanidis

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Papers citing "Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale"

5 / 5 papers shown

Title
Comparison of machine learning algorithms for merging gridded satellite and earth-observed precipitation data Georgia Papacharalampous Hristos Tyralis Anastasios Doulamis N. Doulamis 68 15 0 17 Dec 2022
A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting Georgia Papacharalampous Hristos Tyralis AI4CE 84 28 0 17 Jun 2022
Features of the Earth's seasonal hydroclimate: Characterizations and comparisons across the Koppen-Geiger climates and across continents Georgia Papacharalampous Hristos Tyralis Y. Markonis P. Máca M. Hanel BDL 52 0 0 13 Apr 2022
Time series features for supporting hydrometeorological explorations and predictions in ungauged locations using large datasets Georgia Papacharalampous Hristos Tyralis AI4TS 51 10 0 13 Apr 2022
Hydroclimatic time series features at multiple time scales Georgia Papacharalampous Hristos Tyralis Y. Markonis M. Hanel AI4TS 64 3 0 02 Dec 2021