Hayatijozani, A., Barzandeh, A., Maljutenko, I., Raudsepp, U.

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Convolutional autoencoder latent-space modeling with climate-index–conditioned recurrent networks for assessing subseasonal potential forecast skill of the Baltic Sea heat content

author

Hayatijozani, Ali

Barzandeh, Amirhossein

Maljutenko, Ilja

Raudsepp, Urmas

statement of authorship

Ali Hayatijozani, Amirhossein Barzandeh, Ilja Maljutenko, Urmas Raudsepp

source

Engineering applications of artificial intelligence

publisher

Elsevier

journal volume number month

vol. 176, 2

year of publication

2026

pages

art. 114807

url

https://doi.org/10.1016/j.engappai.2026.114807

subject term

sügavõpe

tehisnärvivõrgud

ilmaennustused

prognostika

merevesi

soojus

subject of location

Läänemeri

keyword

deep learning (DL)

convolutional autoencoder

recurrent neural networks (RNNs)

subseasonal forecasting

ocean heat content

Baltic Sea

ISSN

0952-1976

1873-6769

scientific publication

teaduspublikatsioon

classifier

1.1

TalTech department

meresüsteemide instituut

language

Inglise

Hayatijozani, A., Barzandeh, A., Maljutenko, I., Raudsepp, U. Convolutional autoencoder latent-space modeling with climate-index–conditioned recurrent networks for assessing subseasonal potential forecast skill of the Baltic Sea heat content // Engineering applications of artificial intelligence (2026) vol. 176, 2, art. 114807. https://doi.org/10.1016/j.engappai.2026.114807