Title: Prediction of Sea Surface Temperature using Long Short-Term Memory,
Abstract: This letter adopts long short-term memory(LSTM) to predict sea surface
temperature(SST), which is the first attempt, to our knowledge, to use
recurrent neural network to solve the problem of SST prediction, and to make
one week and one month daily prediction. We formulate the SST prediction
problem as a time series regression problem. LSTM is a special kind of
recurrent neural network, which introduces gate mechanism into vanilla RNN to
prevent the vanished or exploding gradient problem. It has strong ability to
model the temporal relationship of time series data and can handle the
long-term dependency problem well. The proposed network architecture is
composed of two kinds of layers: LSTM layer and full-connected dense layer.
LSTM layer is utilized to model the time series relationship. Full-connected
layer is utilized to map the output of LSTM layer to a final prediction. We
explore the optimal setting of this architecture by experiments and report the
accuracy of coastal seas of China to confirm the effectiveness of the proposed
method. In addition, we also show its online updated characteristics.