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  • Han, Runsheng  (3)
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  • 1
    Online Resource
    Online Resource
    Unsupervised Machine Learning for Improved Delaunay Triangulation
    MDPI AG ; 2021
    In:  Journal of Marine Science and Engineering Vol. 9, No. 12 ( 2021-12-07), p. 1398-
    Details
    In: Journal of Marine Science and Engineering, MDPI AG, Vol. 9, No. 12 ( 2021-12-07), p. 1398-
    Abstract: Physical oceanography models rely heavily on grid discretization. It is known that unstructured grids perform well in dealing with boundary fitting problems in complex nearshore regions. However, it is time-consuming to find a set of unstructured grids in specific ocean areas, particularly in the case of land areas that are frequently changed by human construction. In this work, an attempt was made to use machine learning for the optimization of the unstructured triangular meshes formed with Delaunay triangulation in the global ocean field, so that the triangles in the triangular mesh were closer to equilateral triangles, the long, narrow triangles in the triangular mesh were reduced, and the mesh quality was improved. Specifically, we used Delaunay triangulation to generate the unstructured grid, and then developed a K-means clustering-based algorithm to optimize the unstructured grid. With the proposed method, unstructured meshes were generated and optimized for global oceans, small sea areas, and the South China Sea estuary to carry out data experiments. The results suggested that the proportion of triangles with a triangle shape factor greater than 0.7 amounted to 77.80%, 79.78%, and 79.78%, respectively, in the unstructured mesh. Meanwhile, the proportion of long, narrow triangles in the unstructured mesh was decreased to 8.99%, 3.46%, and 4.12%, respectively.
    Type of Medium: Online Resource
    ISSN: 2077-1312
    URL: Article
    DOI: 10.3390/jmse9121398
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2738390-8
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  • 2
    Online Resource
    Online Resource
    Inversion of Ocean Subsurface Temperature and Salinity Fields Based on Spatio-Temporal Correlation
    MDPI AG ; 2022
    In:  Remote Sensing Vol. 14, No. 11 ( 2022-05-27), p. 2587-
    Details
    In: Remote Sensing, MDPI AG, Vol. 14, No. 11 ( 2022-05-27), p. 2587-
    Abstract: Ocean observation is essential for studying ocean dynamics, climate change, and carbon cycles. Due to the difficulty and high cost of in situ observations, existing ocean observations are inadequate, and satellite observations are mostly surface observations. Previous work has not adequately considered the spatio-temporal correlation within the ocean itself. This paper proposes a new method—convolutional long short-term memory network (ConvLSTM)—for the inversion of the ocean subsurface temperature and salinity fields with the sea surface satellite observations (sea surface temperature, sea surface salinity, sea surface height, and sea surface wind) and subsurface Argo reanalyze data. Given the time dependence and spatial correlation of the ocean dynamic parameters, the ConvLSTM model can improve inversion models’ robustness and generalizability by considering ocean variability’s significant spatial and temporal correlation characteristics. Taking the 2018 results as an example, our average inversion results in an overall normalized root mean square error (NRMSE) of 0.0568 °C/0.0027 PSS and a correlation coefficient (R) of 0.9819/0.9997 for subsurface temperature (ST)/subsurface salinity (SS). The results show that SSTA, SSSA SSHA, and SSWA together are valuable parameters for obtaining accurate ST/SS estimates, and the use of multiple channels in shallow seas is effective. This study demonstrates that ConvLSTM is superior in modeling the subsurface temperature and salinity fields, fully taking global ocean data’s spatial and temporal correlation into account, and outperforms the classic random forest and LSTM approaches in predicting subsurface temperature and salinity fields.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs14112587
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2513863-7
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  • 3
    Online Resource
    Online Resource
    Prediction of significant wave height based on EEMD and deep learning
    Frontiers Media SA ; 2023
    In:  Frontiers in Marine Science Vol. 10 ( 2023-2-22)
    Details
    In: Frontiers in Marine Science, Frontiers Media SA, Vol. 10 ( 2023-2-22)
    Abstract: Accurate and reliable wave significant wave height(SWH) prediction is an important task for marine and engineering applications. This study aims to develop a new deep learning algorithm to accurately predict the SWH of deep and distant ocean. In this study, we combine two methods, Ensemble Empirical Mode Decomposition (EEMD) and Long Short-Term Memory (LSTM), to construct an EEMD-LSTM model, and explore the optimal parameters of the model through experiments. A total of 5328 hours of SWH data from November 30, 2020, to July 9, 2021, are used to train and test the model to predict the SWH for the future 1h, 3h, 6h, 12h, and 18h. The results show that the EEMD-LSTM model has the best results compared with other comparative models for short-term and medium- and long-term predictions. The RMSEs are 0.0204, 0.0279, 0.0452, 0.0941, and 0.1949 for the SWH prediction in the future 1, 3, 6, 12, and 18 h. It can be used as a rapid SWH prediction system to ensure navigation safety to a certain extent, which has great practical significance and application value.
    Type of Medium: Online Resource
    ISSN: 2296-7745
    URL: Article
    DOI: 10.3389/fmars.2023.1089357
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
    detail.hit.zdb_id: 2757748-X
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