Ocean Temperature Prediction Based on Stereo Spatial and Temporal 4-D Convolution Model
Xinyi Zuo, Xiaofeng Zhou, Daquan Guo, Shuai Li, Shurui Liu, Chunhui Xu
Abstract
Ocean temperature prediction has always occupied an important position in the research of ocean-related fields. The current studies are mostly based on the temperature of the sea surface, but the prediction of ocean internal temperature is more important in practical applications. At present, most of the research studies on the prediction of ocean internal temperature are based on time series, few of which consider the dual characteristics of time and space. Therefore, the accuracy is insufficient, especially for the prediction of thermocline and deep-sea locations. This letter proposes the stereo spatial and temporal 4-D convolution model (SST-4D-CNN) to predict the temperature in the ocean, which fully considers the dual characteristics of time series and oceanic spatial relationship to improve the prediction accuracy. The model includes 4-D convolution module, residual module and recalibration module to predict the horizontal and profile temperature changes from the sea surface to 2000-m underwater. In this letter, the prediction experiment is carried out using the real-time analysis data-temperature dataset from National Marine Data Center. The results show that the accuracy of this method in horizontal and profile prediction is above 98.02%, and most of them are more than 99%.