@conference{Multivariate-Autoencoder_2024,
author = "Cosmin M.Marina and Eugenio Lorente-Ramos and Rafael Ayll{\'o}n-Gavil{\'a}n and Pedro Antonio Guti{\'e}rrez and Jorge P{\'e}rez-Aracil and Sancho Salcedo-Sanz",
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 (+1.1257C), while reducing the problem’s dimensionality.",
booktitle = "Advances in Artificial Intelligence",
doi = "https://link.springer.com/chapter/10.1007/978-3-031-62799-6_23",
issn = "1611-3349",
keywords = "Extreme climate events, heat waves, multivariate method, analogue method",
month = "Junio",
pages = "223–232",
title = "{M}ultivariate-{A}utoencoder {F}low-{A}nalogue {M}ethod for {H}eat {W}aves {R}econstruction",
url = "doi.org/10.1007/978-3-031-62799-6_23",
volume = "14640",
year = "2024",
}