Prediction of sea surface height based on recurrent neural network
by Ying Li; Yong Li; Danya Xu; Jingyu Jiang
International Journal of Adaptive and Innovative Systems (IJAIS), Vol. 3, No. 1, 2021

Abstract: The prediction of sea surface height (SSH) is not only theoretically important, but also has many practical applications in various ocean-related fields. Due to the temporal nature of SSH variation, recurrent neural network (RNN) models in deep learning have the ability to capture long-range dependent information in temporal data. Therefore, this paper will attempt to build a SSH prediction model using RNN and compare it with the traditional BP neural network prediction results. The CORA reanalysis of SSH data in the South China Sea is selected for the experiment. The one-day average accuracy of the RNN prediction reached 90.22%.

Online publication date: Mon, 04-Oct-2021

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