Global monthly sea surface temperature forecasting using the SARIMA, LSTM, and GRU models

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dc.authoridBilgili, Mehmet/0000-0002-5339-6120
dc.contributor.authorBilgili, Mehmet
dc.contributor.authorPinar, Engin
dc.contributor.authorDurhasan, Tahir
dc.date.accessioned2026-02-27T07:33:10Z
dc.date.available2026-02-27T07:33:10Z
dc.date.issued2025
dc.description.abstractGlobal warming has become one of the world's most pressing problems in recent years, accompanied by rising sea surface temperature (SST). The SST time series data are an essential component in balancing the energy at the planet's surface. It is of the utmost importance to forecast future SSTs to assist us in better comprehending the climate dynamics and identifying catastrophic circumstances in advance based on historical observations received from earth observation systems. In this sense, monitoring and forecasting SST has become vital for better understanding future climate trends. In this regard, this study proposes a gated recurrent units (GRUs) model, a long short-term memory (LSTM) neural network technique, and a seasonal auto-regressive integrated moving average (SARIMA) statistical model to predict global monthly SST data. According to the findings from the testing procedure, the MAPE values were 0.1377% for the SARIMA model, 0.1374% for the LSTM model, and 0.1390% for the GRU model. All models were found to have MAE, RMSE, and R values within the ranges of 0.0250-0.0253 oC, 0.032-0.0323 oC, and 0.9772-0.9775, respectively. The results of the proposed SARIMA, LSTM, and GRU models showed that they could accurately and satisfactorily predict the global monthly SST time series.
dc.description.sponsorshipClimate Change Institute at the University of Maine
dc.description.sponsorshipThe authors wish to thank the Climate Change Institute at the University of Maine (https://climatereanalyzer.org/) for supplying data.
dc.identifier.doi10.1007/s12145-024-01585-z
dc.identifier.issn1865-0473
dc.identifier.issn1865-0481
dc.identifier.issue1
dc.identifier.urihttp://dx.doi.org/10.1007/s12145-024-01585-z
dc.identifier.urihttps://hdl.handle.net/20.500.14669/4460
dc.identifier.volume18
dc.identifier.wosWOS:001371932000001
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherSpringer Heidelberg
dc.relation.ispartofEarth Science Informatics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20250327
dc.subjectClimate change
dc.subjectClimate dynamics
dc.subjectSea surface temperature
dc.subjectTime series analysis
dc.subjectSARIMA model
dc.subjectLSTM model
dc.subjectGRU model
dc.titleGlobal monthly sea surface temperature forecasting using the SARIMA, LSTM, and GRU models
dc.typeArticle

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