Abnormal high water temperature prediction in nearshore waters around the Korean Peninsula using ECMWF ERA5 data and a deep learning model

Abstract

The abnormally high-water temperature (AHWT) phenomena have caused the mass stranding of farmed fish in the Korean coastal waters, leading to a substantial monetary loss in recent decades. It is most important to predict the HWT occurrence and take responsive measures before the HWT arrival to prevent such loss, we proposed a methodology to predict HWT occurrences using a deep-learning technology. Firstly, we trained a long short-term memory (LSTM) deep-learning model using the sea surface temperature data from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 product to estimate future water temperature in advance. Secondly, we used the estimated water temperature data to predict HWT occurrences from 1 day to 7 days later. We computed root mean square error (RMSE), mean absolute percentage error (MAPE) metrics, and F1-scores to evaluate the accuracy of the proposed LSTM model. In the cases of 1-day and 7-day water temperature predictions, RMSE and MAPE values between the estimated data and the sea-truth data were 0.293 degrees Celsius with 1.313 % and 0.854 degrees Celsius with 4.175 %, respectively. The F1-scores of the classification algorithm of 1- and 7-day HWT predictions were 0.96 and 0.74, respectively. This study contributes to developing measures to reduce the monetary loss of HWT damage on fish farms.