Abstract
This paper contributes with an alternative to the multivariate Analogue Method (AM) version, using a preprocessing stage carried out by an Autoencoder (AE). The proposed method (MvAE-AM) is applied to reconstruct France’s 2003, Balkans’ 2007 and Russia 2010 mega heat waves. Using divers such as geopotential height of the 500hPA (Z500), mean sea level pressure (MSL), soil moisture (SM), and potential evaporation (PEva), the AE extracts the most relevant information into a smaller univariate latent space. Then, the classic univariate AM is applied to search for similar situations in the past over the latent space, with a minimum distance to the heat wave under evaluation. We have compared the proposed method’s performance with that of a classical multivariate AM (MvAM), showing that the MvAE-AM approach outperforms the MvAM in terms of accuracy (\(+1.1257\)C), while reducing the problem’s dimensionality.
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Acknowledgements
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. The present study has been partially supported by the “Agencia Estatal de Investigación (España)” (grant ref.: PID2020-115454GB-C21 and PID2020-115454GB-C22 through the projects of the Spanish Ministry of Science and Innovation (MICINN). David Guijo-Rubio has been supported by the “Agencia Estatal de Investigación (España)” MCIU/AEI/10.13039/501100011033 and European Union NextGenerationEU/PRTR (grant ref.: JDC2022-048378-I).
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Marina, C.M., Lorente-Ramos, E., Ayllón-Gavilán, R., Gutiérrez, P.A., Pérez-Aracil, J., Salcedo-Sanz, S. (2024). Multivariate-Autoencoder Flow-Analogue Method for Heat Waves Reconstruction. In: Alonso-Betanzos, A., et al. Advances in Artificial Intelligence. CAEPIA 2024. Lecture Notes in Computer Science(), vol 14640. Springer, Cham. https://doi.org/10.1007/978-3-031-62799-6_23
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