Abnormally High Water Temperature Prediction using Deep Learning Technology

Abstract

Recently, abnormally high water temperature (AHWT) phenomena have caused extensive damage to the maritime economy of Korea. These phenomena have caused the mass stranding of farmed fish around the Korean coast and have also caused illnesses by facilitating the propagation of Vibrio pathogens. To reduce damage by AHWT phenomena, it is necessary to respond as quickly as possible or to predict such events in advance. Therefore, in this study, a methodology using satellite big data and a deep-learning technology was proposed to forecast AHWT occurrences. First, the deep-learning model was trained using the long short-term memory (LSTM) architecture. Then, AHWT occurrences were predicted using the trained model. It is expected that the use of this methodology will effectively reduce the damage from AHWT phenomena and protect the maritime economy.