Global temperature anomaly prediction by using additive twin LSTM networks

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dc.contributor.authorKeles, Cemal
dc.contributor.authorBaran, Burhan
dc.contributor.authorAlagoz, Baris Baykant
dc.date.accessioned2026-04-04T13:34:40Z
dc.date.available2026-04-04T13:34:40Z
dc.date.issued2026
dc.departmentİnönü Üniversitesi
dc.description.abstractDue to the complexity of climate systems, data-driven modeling based on observed time series data is essential for predicting future climatic trends. This study aims to improve the long-term global temperature anomaly forecast performance of Long Short-Term Memory (LSTM) based neural network models. Although several LSTM variants and hybrid architectures have been suggested for time series data prediction problems, the long-term forecast performance of these models may not be satisfactory in practice. To address solution of these problems, firstly, authors focused on evaluating the forecast performance of models and suggested performance and test assessment procedures. Secondly, authors suggest an Additive Twin LSTM (AT-LSTM) model that can improve the forecast performance for the global temperature anomaly. Our test on the Berkeley Global Temperature Anomaly dataset demonstrates that the proposed AT-LSTM can improve performance relative to conventional LSTM variants in long-term forecasting. Authors observed that global temperature trend projections of the AT-LSTM models for 20 years in future are consistent with expectations of climate organizations and projections in other works. The AT-LSTM models forecasted an average of 1.415 degrees C with +/- 0.073 degrees C error in the year 2042 and this indicates the strong potential of major climate changes in the near future of Earth.
dc.description.sponsorshipInonu University Scientific Research Projects Coordination Unit [FBA-2025-4318]
dc.description.sponsorshipThis work was supported by the Inonu University Scientific Research Projects Coordination Unit (Project No. FBA-2025-4318).
dc.identifier.doi10.1038/s41598-026-37255-x
dc.identifier.issn2045-2322
dc.identifier.issue1
dc.identifier.pmid41606291
dc.identifier.scopus2-s2.0-105030148892
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1038/s41598-026-37255-x
dc.identifier.urihttps://hdl.handle.net/11616/109318
dc.identifier.volume16
dc.identifier.wosWOS:001693299600024
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherNature Portfolio
dc.relation.ispartofScientific Reports
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250329
dc.subjectClimate change
dc.subjectGlobal warming
dc.subjectTemperature anomaly
dc.subjectForecasting
dc.titleGlobal temperature anomaly prediction by using additive twin LSTM networks
dc.typeArticle

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