Prediction of the sea surface temperature in Perhentian Islands by EMD-LSTM model

Abstract

Abstract The increase in the sea surface temperature (SST) is currently an important factor in the decline of coral reef ecosystems worldwide, and SST prediction has always been an important research direction in operational oceanography. This paper collects and analyses the buoy data deployed at Malaysian Perhentian islands and combines CRW data to develop coral bleaching warning products for Malaysian Perhentian islands. The Long Short-Term Memory Network(LSTM) and Empirical Mode Decomposition(EMD)-LSTM methods are used to research the SST prediction, and the differences between the two prediction methods were compared. The research results show that both the LSTM prediction model and the EMD-LSTM prediction model can accurately predict SST, with almost all prediction errors at 0.11°C and 0.01°C, respectively.