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Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
EN
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EL
| URI | http://purl.tuc.gr/dl/dias/8D690926-FF07-44DB-A6B3-4F2A674563DF | - |
| Identifier | https://doi.org/10.3390/e24030321 | - |
| Identifier | https://www.mdpi.com/1099-4300/24/3/321 | - |
| Language | en | - |
| Extent | 21 pages | en |
| Title | Spatial modeling of precipitation based on data-driven warping of Gaussian processes | en |
| Creator | Agou Vasiliki | en |
| Creator | Αγου Βασιλικη | el |
| Creator | Pavlidis Andreas | en |
| Creator | Παυλιδης Ανδρεας | el |
| Creator | Christopoulos Dionysios | en |
| Creator | Χριστοπουλος Διονυσιος | el |
| Publisher | MDPI | en |
| Content Summary | 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. | en |
| Type of Item | Peer-Reviewed Journal Publication | en |
| Type of Item | Δημοσίευση σε Περιοδικό με Κριτές | el |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2023-08-22 | - |
| Date of Publication | 2022 | - |
| Subject | Non-Gaussian data | en |
| Subject | Skewed distributions | en |
| Subject | Gaussian anamorphosis | en |
| Subject | Reanalysis data | en |
| Subject | Kriging | en |
| Subject | Warped Gaussian processes | en |
| Bibliographic Citation | 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. | en |
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