Global monthly sea surface temperature forecasting using the SARIMA, LSTM, and GRU models
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Tarih
2025
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Heidelberg
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Global 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.
Açıklama
Anahtar Kelimeler
Climate change, Climate dynamics, Sea surface temperature, Time series analysis, SARIMA model, LSTM model, GRU model
Kaynak
Earth Science Informatics
WoS Q Değeri
Scopus Q Değeri
Cilt
18
Sayı
1
