Real-Time Prediction of Ocean Observation Data Based on Transformer Model
Wenqing Chang, Xiang Li, Huomin Dong, Chunxiao Wang, Zhigang Zhao, Yinglong Wang · 2021
Ocean monitoring depends on the widely deployed oceanographic buoys and observation stations that integrate various types of ocean sensors. Those ocean sensors often work in harsh environments, so the data collected by the sensors are sometimes abnormal, which affects the accuracy of downstream applications, e.g., the ocean data assimilation and intelligent data mining. It is not realistic to conduct abnormal data detection (quality control) on huge amounts of increasing data only rely on human-based services. Hence, current research tendency is adopting AI models to realize automatic quality control, which largely depends on the accuracy of the AI model in ocean data modeling and prediction. Hence, in order to accurately model and predict the observation data, this paper explores and compares various sequential data modeling methods, which are widely adopted in time series data forecasting. We further proposes a Transformer model for accurate real-time prediction of those data. The experimental results show that our proposed method is better than the baselines both in single-step and multiple-step prediction experimental settings. As far as we know, this is the first work that applying transformer based model in ocean observation data intelligent analysis.