SDPNet: A Novel DeepLearning Method for Ocean Surface Current Prediction

Abstract

Abstract Ocean surface currents (OSC), abiotic features of the environment, are continuous and directed movements of ocean water. Prediction to OSC is of significant interests in physical oceanography. Recently, deep learning technology has shown feasibility in mining the intrinsic change pattern of marine numerical values, such as SSS, SST and SSHA, but not been applied to OSC prediction yet. In this work, a deep learning method, named skipped dual path network (SDPNet), is proposed for OSC prediction. Specifically, SDPNet has a convolutional neural network (CNN) module with a one-dimensional convolution layer, and a recurrent neural network (RNN) module with a dual-path structure. Each path consists of a stack of three layers of long short-term memory (LSTM) and gated recurrent unit (GRU), respectively. As well, a skipped-connection structure is added in the both paths. It aims to gradually mine the intrinsic change pattern contained in OSC time series data itself. Experiments are conducted on the South China Sea OSC data set in REDOS. SDPNet achieves accuracy 80.83%, 75.9%, 74.9%, 73.9%, 72.58%, 70.35%, 69.93% in predicting the coming 7 days OSC values and directions. It performs better than state-of-the-art machine learning methods, include Artificial Neural Network, Simple RNN, LSTM and GRU.