Abstract
Sea surface height anomaly (SSHA) is an elemental factor in ocean environment and marine engineering. Oceanography models can forecast SSH by data simulation, but the accuracy decreases heavily when it predicts a little long time ahead. In this article, a deep learning method, named merged-long short term memory (LSTM), is proposed to predict SSHA. Specifically, SSHA prediction is treated as a time series forecasting problem, and our merged-LSTM can mine the discipline hidden in short time series, and tackle long-term dependence of series changes. Data experiments conducted on SSHA dataset of China Ocean Reanalysis in the South China Sea show that our method achieves average predicting accuracy plus/minus standard deviation of coming 24 h, 48 h, 72 h, 96 h, and 120 h by 90.99±10.56%, 85.49±13.93%, 79.99±16.08%, 74.23±18.05%, 68.15±18.84%, respectively. The proposed method performs better than several state-of-the-art machine learning methods, including artificial neural network, merged-recurrent neural network, time convolutional network, merged-gate recurrent unit, and one-dimensional convolutional neural network in predicting SSHA.
Highlights
S EA surface height anomaly (SSHA) is a vital factor to ship navigation [1], fishery resource forecasting [2], and marine engineering and industry [3]
Artificial intelligent (AI) methods provide a group of data-driven approaches to “learn” the discipline hidden in the time series SSHA data
SSHA prediction is treated as a time series prediction problem, and a merged-long short term memory (LSTM) model is proposed, which is capable to mine the discipline hidden in the short time series and tackle the long-term dependence of series changes
Summary
Abstract—Sea surface height anomaly (SSHA) is an elemental factor in ocean environment and marine engineering. A deep learning method, named merged-long short term memory (LSTM), is proposed to predict SSHA. SSHA prediction is treated as a time series forecasting problem, and our merged-LSTM can mine the discipline hidden in short time series, and tackle long-term dependence of series changes. Data experiments conducted on SSHA dataset of China Ocean Reanalysis in the South China Sea show that our method achieves average predicting accuracy plus/minus standard deviation of coming 24 h, 48 h, 72 h, 96 h, and 120 h by 90.99±10.56%, 85.49±13.93%, 79.99±16.08%, 74.23±18.05%, 68.15±18.84%, respectively. The proposed method performs better than several state-of-the-art machine learning methods, including artificial neural network, merged-recurrent neural network, time convolutional network, merged-gate recurrent unit, and one-dimensional convolutional neural network in predicting SSHA
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have