Deep-learning model for sea surface temperature prediction near the Korean Peninsula

Abstract

Recently, sea surface temperatures (SSTs) near the Korean Peninsula have increased rapidly due to global warming; this phenomenon can lead to high water temperatures and extensive damage to Korean fish farms. To reduce such damage, it is necessary to predict high water temperature events in advance. In this study, we developed a method for predicting high water temperature events using time series SST data for the Korean Peninsula obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 product and a long short-term memory (LSTM) network designed for time series data prediction. First, the SST prediction model was used to predict SSTs. Predicted SSTs exceeding 28 °C, which is the Korean government standard for issuing high water temperature warnings, were designated as high water temperatures. To evaluate the prediction accuracy of the SST prediction model, 1-to 7-day predictions were evaluated in terms of the coefficient of determination (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE). The R2, RMSE, and MAPE values of the 1-day prediction SST model were 0.985, 0.14 °C, and 0.38%, respectively, whereas those of the 7-day prediction SST model were 0.574, 0.74 °C, and 2.26%, respectively. We also calculated F1 scores to evaluate high water temperature classification accuracy. The F1 scores of the 1- and 7-day SST prediction models were 0.963 and 0.739, respectively.