| DOI | Trouver le DOI : https://doi.org/10.1109/SSCI50451.2021.9660188 |
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| Auteur | Rechercher : Valdes, Julio J.1; Rechercher : Pou, Antonio |
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| Affiliation | - Conseil national de recherches du Canada. Technologies numériques
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| Format | Texte, Article |
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| Conférence | 2021 IEEE Symposium Series on Computational Intelligence (SSCI), December 5-7, 2021, Orlando, FL, USA |
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| Sujet | image quality; visualization; satellites; atmospheric measurements; planets; atmospheric modeling; weather forecasting; computational intelligence; water vapor satellite images; VIFp image similarity; intrinsic dimension; low-dimensional mappings; supervised modeling; explainable AI; climate change |
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| Résumé | Water vapor in the atmosphere plays a crucial role in the energy balance and weather, being responsible for half of the greenhouse effect. Meteorological satellites detect the water vapor and represent it on images, providing important information to understand and forecast the flow dynamics of the General Atmospheric Circulation System. A collection of computational intelligence techniques was used to investigate the structure of a large series of Meteosat (ESA) water vapor band (WV6.2) hourly images from 2009 to 2020. These techniques include the Visual Information Fidelity image quality measure, unsupervised and supervised machine learning and explainable AI methods. Explainable AI methods (XAI) like Permutational Variable Importance, Local Interpretable Model-Agnostic Explanations, Shapley Additive Explanations and Ceteris Paribus profiles, were able to discover temporal variations and changes on the water vapor patterns. The results obtained demonstrate the great potential of ML and XAI in the domain of atmosphere dynamics and weather evolution. |
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| Date de publication | 2021-12-05 |
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| Maison d’édition | IEEE |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 26ee42d6-a1ab-4f25-aea1-e9040224c0ab |
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| Enregistrement créé | 2022-02-14 |
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| Enregistrement modifié | 2022-02-14 |
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