Time-Series Hourly Sea Surface Temperature Prediction Using Deep Neural Network Models

Sea surface temperature (SST) is crucial in ocean research and marine activities. It makes predicting SST of paramount importance. While highly affected by different oceanic, atmospheric, climatic parameters, few papers have investigated time-series prediction based on multiple features. This paper utilized multi features air pressure, water temperature, wind direction, speed for hourly using deep neural networks convolutional network (CNN), long short-term memory (LSTM), CNN–LSTM. Models were trained validated epochs, feature importance was evaluated the leave-one-feature-out method. Air pressure significantly more important than direction speed. Accordingly, selection an essential step prediction. Findings also revealed that all models performed well with low errors, increasing epochs did not necessarily improve modeling. similarly practical, CNN considered most suitable as its training several times faster other two models. With this, variance data helped make accurate predictions, proposed method may higher errors while working variant