Uncertainty estimation in spatial interpolation of satellite precipitation with ensemble learning

arXiv:2403.10567v1 Announce Type: new
Abstract: Predictions in the form of probability distributions are crucial for decision-making. Quantile regression enables this within spatial interpolation settings for merging remote sensing and gauge precipitation data. However, ensemble learning of quantile regression algorithms remains unexplored in this context. Here, we address this gap by introducing nine quantile-based ensemble learners and applying them to large precipitation datasets. We employed a novel feature engineering strategy, reducing predictors to distance-weighted satellite precipitation at relevant locations, combined with …

abstract algorithms arxiv context cs.lg data decision ensemble form gap however making merging precipitation predictions probability quantile regression satellite sensing spatial stat.me type uncertainty