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  • Prospect of artificial intelligence in oceanography
    DONG Changming, WANG Ziyun, XIE Huarong, XU Guangjun, HAN Guoqing, ZHOU Shuyi, XIE Wenhong, SHEN Xiangyu, HAN Lei

    Artificial intelligence in oceanography has demonstrated a great potential with the explosive growth of ocean observation data and numerical model products. This article first reviews the history of ocean big data development, and then introduces in detail the current status of artificial intelligence in oceanography applications including identifying ocean phenomenon, forecasting ocean variables and phenomenon, estimating dynamic parameters, correcting forecast errors, and solving dynamic equations. Specifically, this article elaborates the research on the intelligent identification of ocean eddies, internal waves and sea ice, the intelligent prediction of sea surface temperatures, El Ni?o-Southern Oscillation, storm surges, waves and currents, the intelligent estimation of ocean turbulence parameterization for numerical models, and the intelligent correction of waves and current forecast errors. In addition, it discusses the recent progress of applying physical mechanism fusion and Fourier neural operator for solving ocean dynamic equations. This article is based on the current status of artificial intelligence in oceanography and aims to provide a comprehensive demonstration of the advantages and potential of applying artificial intelligence methods in the field of oceanography. With the two emerging research hotspots: digital twin oceans and artificial intelligence large models, the future development direction of artificial intelligence provides enlightenment and reference for interested scientists and researchers.

  • Progress and challenges of artificial intelligence wave forecasting
    LU Yuting, GUO Wenkang, DING Jun, WANG Linfeng, LI Xiaohui, WANG Jiuke

    Waves are one of the most important phenomena in the ocean. The accurate and quick updated wave forecasting is of crucial significance for ensuring marine activities safety. The development of wave forecast is presented, including the traditional statistical wave forecasting methods, numerical wave prediction models, and the rapidly developing artificial intelligence (AI) wave forecasting methods. Currently, AI wave forecast models have been demonstrated unique advantages in terms of computational efficiency and adaptive forecasting accuracy, and they are gradually being applied in practical wave forecasting operations, transitioning from the research stage. However, they also have limitations, including limited forecasting elements, underestimation of extreme wave conditions, and weak forecasting generalization ability. Based on the characteristics of AI wave prediction, key scientific and technological issues that need to be addressed in current AI wave forecasting are proposed. These include efficient utilization of observational data, incorporation of prior physical knowledge, and enhancement of AI model safety and generalization ability.

  • Histopathological indexes can be used to assess health of organism, but their application faces challenges such as low efficiency, high cost and strong subjectivity. Introducing artificial intelligence (AI) technology into histopathological analysis of biological tissues can leverage its high-throughput image analysis capabilities, overcoming the limitations in assessing and monitoring marine organism health. Based on our review on health assessment indicators of marine organisms, the application of AI in image analysis, and the use of AI for histopathological image processing, a deep learning-based histopathological image analysis approach was proposed using gill tissues of marine mussels as representative. Through a series of processes such as training, validation, and prediction of histopathology images, it was determined that the Res-UNet deep learning model can efficiently and accurately quantify histopathological damage in mussels’ gills. An automated, high-throughput, and less subjective workflow based on deep learning was finally established, offering new ideas and techniques for marine organism health assessment and marine monitoring.

  • Prospect of artificial intelligence in oceanography
    DONG Changming, WANG Ziyun, XIE Huarong, XU Guangjun, HAN Guoqing, ZHOU Shuyi, XIE Wenhong, SHEN Xiangyu, HAN Lei
    2024, 42(3):2-27. DOI:10.3969/j.issn.1001-909X.2024.03.001
    Abstract ( 84 ) HTML ( 5 ) PDF ( 4260KB ) ( 30 )   
  • Progress and challenges of artificial intelligence wave forecasting
    LU Yuting, GUO Wenkang, DING Jun, WANG Linfeng, LI Xiaohui, WANG Jiuke
    2024, 42(3):28-37. DOI:10.3969/j.issn.1001-909X.2024.03.002
    Abstract ( 56 ) HTML ( 5 ) PDF ( 1059KB ) ( 13 )   
  • Recent developments in AI-based oceanic eddy identification
    XU Guangjun, SHI Yucheng, YU Yang, XIE Huarong, XIE Wenhong, LIU Jingyuan, LIN Xiayan, LIU Yu, DONG Changming
    2024, 42(3):38-50. DOI:10.3969/j.issn.1001-909X.2024.03.003
    Abstract ( 63 ) HTML ( 4 ) PDF ( 2744KB ) ( 15 )   
  • Review of application of deep learning in Indian Ocean Dipole prediction
    ZHENG Mengke, FANG Wei, ZHANG Xiaozhi
    2024, 42(3):51-63. DOI:10.3969/j.issn.1001-909X.2024.03.004
    Abstract ( 77 ) HTML ( 3 ) PDF ( 2495KB ) ( 20 )   
  • Deep learning-based histopathological analysis and its potential application in marine monitoring: A review and case study
    DI Ya’nan, ZHAO Ruoxuan, XU Jianzhou
    2024, 42(3):64-74. DOI:10.3969/j.issn.1001-909X.2024.03.005
    Abstract ( 34 ) HTML ( 4 ) PDF ( 2695KB ) ( 16 )   
  • Calibration of Sentinel-1 SAR retrieved wind speed based on BP neural network model
    NI Hanyue, DONG Changming, LIU Zhenbo, YANG Jingsong, LI Xiaohui, REN Lin
    2024, 42(3):75-87. DOI:10.3969/j.issn.1001-909X.2024.03.006
    Abstract ( 52 ) HTML ( 6 ) PDF ( 3937KB ) ( 25 )   
  • Intelligent wave forecasting and evaluation along the southeast coast of China based on ConvLSTM method
    JIN Yang, HAN Lei, JIN Meibing, DONG Changming
    2024, 42(3):88-98. DOI:10.3969/j.issn.1001-909X.2024.03.007
    Abstract ( 19 ) HTML ( 2 ) PDF ( 4833KB ) ( 10 )   
  • Rapid intensification forecast of tropical cyclones based on machine learning
    LUO Tong, HONG Jiacheng
    2024, 42(3):99-107. DOI:10.3969/j.issn.1001-909X.2024.03.008
    Abstract ( 34 ) HTML ( 3 ) PDF ( 2298KB ) ( 25 )   
  • Prediction of sea level changes along the coast of China using machine learning models
    CHEN Jianheng, XU Dongfeng, YAO Zhixiong
    2024, 42(3):108-118. DOI:10.3969/j.issn.1001-909X.2024.03.009
    Abstract ( 24 ) HTML ( 4 ) PDF ( 2235KB ) ( 17 )   
  • Classification accuracy and influencing factors of Arctic sea ice based on deep learning and Sentinel-1 satellite imagery
    SHAO Zhiyuan, ZHAO Jiechen, XIE Longxiang, MU Fangru, XIAO Jing, LIU Minjun, CHEN Xuejing
    2024, 42(3):119-130. DOI:10.3969/j.issn.1001-909X.2024.03.010
    Abstract ( 21 ) HTML ( 0 ) PDF ( 4482KB ) ( 9 )   
  • Application of convolutional neural network method in evolution of tidal bore hydrodynamic characteristics
    WANG Zhihong, QU Ke, YANG Yuanping, WANG Xu, GAO Rongze
    2024, 42(3):131-141. DOI:10.3969/j.issn.1001-909X.2024.03.011
    Abstract ( 22 ) HTML ( 0 ) PDF ( 2488KB ) ( 19 )   
  • High-precision seafloor topographic mapping based on data-knowledge-driven: An example from the South China Sea
    LIU Yang, LI Sanzhong, ZOU Zhuoyan, SUO Yanhui, SUN Yi
    2024, 42(3):142-152. DOI:10.3969/j.issn.1001-909X.2024.03.012
    Abstract ( 17 ) HTML ( 2 ) PDF ( 3329KB ) ( 16 )   
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