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cs.LG2024
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…
cs.LG2024
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…