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Multi-source and heterogeneous marine hydrometeorology spatio-temporal data analysis with machine learning: a survey

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Abstract

There is a new trend in marine hydrometeorology (MHM) that calls for novel solutions on massive multi-source and heterogeneous spatiotemporal data sets. Traditionally, the research and analysis of MHM objects are generally based on statistical analysis of observation data, numerical simulation, laboratory observation and experiments. However, these methods fail to adopt onto the massive multi-source and multi-modality data analysis problem. To better understand and analyze the new requirements on MHM data, researchers started a data-oriented approach that mines features and patterns from the massive datasets, or so called the machine learning approach. In this paper, we provide a systematic review on the applicability of machine learning approaches in understanding and mining MHM objects. We start with these techniques from the perspective of different data sources and focusing on learning objects like oceanic internal wave, tide, sea ice, typhoon, and red tide from recognition to prediction. First, this paper systematically summarizes the current research methodologies, unique data characteristics, and the challenges of machine learning in MHM. Next, we classify the mainstream data and models, and overview the machine learning models from the perspective of different MHM scenarios and multiple data sources. Then, we summarize the key techniques, with pros and cons of machine learning applications in processing such scenarios. Last, we conclude with the future research trend of machine learning in MHM, especially in model interpretability.

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (Grant No. 2018YFB0203801), and the National Natural Science Foundation of China (Grant Nos. 62032019, 61572510, 61702529, 61502511), and China National Special Fund for Public Welfare (Grant No. GYHY201306003).

Funding

This work was supported by the National Key Research and Development Program of China (Grant No. 2018YFB0203801), and the National Natural Science Foundation of China (Grant Nos. 62032019, 61572510, 61702529, 61502511), and China National Special Fund for Public Welfare (Grant No. GYHY201306003).

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Authors and Affiliations

  1. College of Computer Science and Technology, National University of Defense Technology, Changsha, 410073, Hunan, China

    Song Wu & Wei Dong

  2. College of Meteorology and Oceanography, National University of Defense Technology, Changsha, 410073, Hunan, China

    Song Wu, Xiaoyong Li & Xiaojiang Zhang

  3. School of computer science and engineering, Central South University, Changsha, 410012, Hunan, China

    Senzhang Wang

  4. College of Mathematics and Computer Science, Nanchang University, Nanchang, 330031, Jiangxi, China

    Zichen Xu

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  1. Song Wu
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Contributions

Author Song Wu is responsible for literature collection and writing. Author Xiaoyong Li and Wei Dong are responsible for writing ideas and revising the paper. Author Senzhang Wang, Xiaojiang Zhang, Zichen Xu are responsible for giving guidance and revising the paper. All authors reviewed the manuscript and agreed with the content.

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Correspondence to Xiaoyong Li.

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Wu, S., Li, X., Dong, W. et al. Multi-source and heterogeneous marine hydrometeorology spatio-temporal data analysis with machine learning: a survey. World Wide Web 26, 1115–1156 (2023). https://doi.org/10.1007/s11280-022-01069-4

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