Το έργο με τίτλο Spatial modeling of precipitation based on data-driven warping of Gaussian processes από τον/τους δημιουργό/ούς Agou Vasiliki, Pavlidis Andreas, Christopoulos Dionysios διατίθεται με την άδεια Creative Commons Αναφορά Δημιουργού 4.0 Διεθνές
Βιβλιογραφική Αναφορά
V. D. Agou, A. Pavlides, and D. T. Hristopulos, “Spatial modeling of precipitation based on data-driven warping of Gaussian processes,” Entropy, vol. 24, no. 3, Feb. 2022, doi: 10.3390/e24030321.
https://doi.org/10.3390/e24030321
Modeling and forecasting spatiotemporal patterns of precipitation is crucial for managing water resources and mitigating water-related hazards. Globally valid spatiotemporal models of precipitation are not available. This is due to the intermittent nature, non-Gaussian distribution, and complex geographical dependence of precipitation processes. Herein we propose a data-driven model of precipitation amount which employs a novel, data-driven (non-parametric) implementation of warped Gaussian processes. We investigate the proposed warped Gaussian process regression (wGPR) using (i) a synthetic test function contaminated with non-Gaussian noise and (ii) a reanalysis dataset of monthly precipitation from the Mediterranean island of Crete. Cross-validation analysis is used to establish the advantages of non-parametric warping for the interpolation of incomplete data. We conclude that wGPR equipped with the proposed data-driven warping provides enhanced flexibility and—at least for the cases studied– improved predictive accuracy for non-Gaussian data.