Sea Surface Temperature and High Water Temperature Occurrence Prediction Using a Long Short-Term Memory Model

Remote Sensing

Jonghwa Kim¹  Minkyu Kim¹  Hyun Yang¹ 

[1] School of Ocean Science and Technology, Korea Maritime and Ocean University, Busan 49112, Korea;

关键词: high water temperature;    HWT;    long short-term memory;    LSTM;    recurrent neural network;    RNN;   

DOI  :  10.3390/rs12213654

来源: DOAJ

【 摘 要 】

Recent global warming has been accompanied by high water temperatures (HWTs) in coastal areas of Korea, resulting in huge economic losses in the marine fishery industry due to disease outbreaks in aquaculture. To mitigate these losses, it is necessary to predict such outbreaks to prevent or respond to them as early as possible. In the present study, we propose an HWT prediction method that applies sea surface temperatures (SSTs) and deep-learning technology in a long short-term memory (LSTM) model based on a recurrent neural network (RNN). The LSTM model is used to predict time series data for the target areas, including the coastal area from Goheung to Yeosu, Jeollanam-do, Korea, which has experienced frequent HWT occurrences in recent years. To evaluate the performance of the SST prediction model, we compared and analyzed the results of an existing SST prediction model for the SST data, and additional external meteorological data. The proposed model outperformed the existing model in predicting SSTs and HWTs. Although the performance of the proposed model decreased as the prediction interval increased, it consistently showed better performance than the European Center for Medium-Range Weather Forecast (ECMWF) prediction model. Therefore, the method proposed in this study may be applied to prevent future damage to the aquaculture industry.

【 授权许可】

Unknown