Prediction and analysis of sea surface temperature based on LSTM-transformer model

Abstract

This paper introduces a novel method for predicting sea surface temperature (SST) using a hybrid LSTM-Transformer model. The study utilizes the ERA5 dataset for SST prediction at six specific locations near China. The LSTM-Transformer model combines the temporal processing capability of LSTM with the efficient data processing power of Transformer, showing superior performance in reducing Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), as well as improving the R-squared (R²) value, compared to standalone LSTM, Transformer, and traditional Logistic Regression (LR) models. This is particularly evident in spring and autumn, indicating its robustness to seasonal changes. The model's performance varies across different geographic locations, with lower prediction errors in low-latitude and open sea areas, attributed to the less complex environmental dynamics compared to continental shelf areas. Overall, the LSTM-Transformer hybrid model presents a significant advancement in SST prediction, providing important implications for fisheries, meteorology, and climate change research.