Deep learning for ocean temperature forecasting: a survey

Abstract

Ocean temperature prediction is significant in climate change research and marine ecosystem management. However, relevant statistical and physical methods focus on assuming relationships between variables and simulating complex physical processes of ocean temperature changes, facing challenges such as high data dependence and insufficient processing of long-term dependencies. This paper comprehensively reviews the development and latest progress of ocean temperature prediction models based on deep learning. We first provide a formulaic definition for ocean temperature prediction and a brief overview of deep learning models widely used in this field. Using data sources and model structures, we systematically divide ocean temperature prediction models into data-driven deep learning models and physically guided deep learning models; and comprehensively explore the relevant literature involved in each method. In addition, we summarize an ocean temperature dataset and sea areas, laying a solid foundation for ocean temperature prediction. Finally, we propose current challenges and future development directions in ocean temperature prediction research based on deep learning. This article aims to analyze existing research, identify research gaps and challenges, provide complete and reliable technical support for climate forecasting, marine disaster prevention, and fishery resource management, and promote the further development of ocean temperature research.

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Funding

National Natural Science Foundation of China(62176243)