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ToolsGómez-Orellana, Antonio Manuel, Guijo-Rubio, David, Pérez-Aracil, Jorge, Gutiérrez, Pedro Antonio, Salcedo-Sanz, Sancho and Hervás-Martínez, César (2023) One month in advance prediction of air temperature from Reanalysis data with eXplainable Artificial Intelligence techniques. Atmospheric Research, 284. ISSN 0169-8095
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Abstract
In this paper we have tackled the problem of long-term air temperature prediction with eXplainable Artificial Intelligence (XAI) models. Specifically, we have evaluated the performance of an Artificial Neural Network (ANN) architecture with sigmoidal neurons in the hidden layer, trained by means of an evolutionary algorithm (Evolutionary ANNs, EANNs). This XAI model architecture (XAI-EANN) has been applied to the long-term air temperature prediction at different sub-regions of the South of the Iberian Peninsula. In this case, the average August air temperature has been predicted from ERA5 Reanalysis data variables, obtaining good predictions skills and explainable models in terms of the input climatological variables considered. A cluster analysis has been first carried out in terms of the average air temperature in the zone, in such a way that a number of sub-regions with different air temperature behaviour have been defined. The proposed XAI-EANN model architecture has been applied to each of the defined sub-regions, in order to find significant differences among them, which can be explained with the XAI-EANN models obtained. Finally, a comprehensive comparison against some state-of-the-art techniques has also been carried out, concluding that there are statistically significant differences in terms of accuracy in favour of the proposed XAI-EANN model, which also benefits from being an XAI model.
| Item Type: | Article |
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| Additional Information: | Funding Information: This research has been partially supported by the European Union, through H2020 Project “CLIMATE INTELLIGENCE Extreme events detection, attribution and adaptation design using machine learning (CLINT)”, Ref: 101003876-CLINT (Sancho Salcedo-Sanz). This research has also been partially supported by the projects PID2020-115454 GB-C21 and PID2020-115454 GB-C22 of the Spanish Ministry of Science and Innovation (MICINN) . This work was also partially supported by the “Consejería de Salud y Familia (Junta de Andalucía)” (grant reference: PS-2020–780) and the “Consejería de Transformación Económica, Industria, Conocimiento y Universidades (Junta de Andalucía) y Programa Operativo FEDER 2014–2020” (grant reference: PY20_00074). Antonio M. Gómez-Orellana’s research has been supported by “Consejería de Transformación Económica, Industria, Conocimiento y Universidades de la Junta de Andalucía” (Grant Ref. PREDOC-00489). David Guijo-Rubio’s research is supported by the University of Córdoba through grants to Public Universities for the requalification of the Spanish university system of the Ministry of Universities, financed by the European Union - NextGenerationEU (grant reference: UCOR01MS ). The open access charge was fully supported by the Universidad de Córdoba / CBUA. Publisher Copyright: © 2023 The Authors |
| Uncontrolled Keywords: | air temperature,climatology,long-term air temperature prediction,neural networks,xai,atmospheric science ,/dk/atira/pure/subjectarea/asjc/1900/1902 |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 12 Jan 2023 16:30 |
| Last Modified: | 09 Mar 2024 08:31 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/90550 |
| DOI: | 10.1016/j.atmosres.2023.106608 |
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