A Multi-Spatial Scale Ocean Sound Speed Prediction Method Based on Deep Learning DOI Creative Commons
Yü Liu, Benjun Ma, Zhiliang Qin

и другие.

Journal of Marine Science and Engineering, Год журнала: 2024, Номер 12(11), С. 1943 - 1943

Опубликована: Окт. 31, 2024

As sound speed is a fundamental parameter of ocean acoustic characteristics, its prediction central focus underwater acoustics research. Traditional numerical and statistical forecasting methods often exhibit suboptimal performance under complex conditions, whereas deep learning approaches demonstrate promising results. However, these methodologies fall short in adequately addressing multi-spatial coupling effects spatiotemporal weighting, particularly scenarios characterized by limited data availability. To investigate the interactions across multiple spatial scales to achieve accurate predictions, we propose STA-ConvLSTM framework that integrates attention mechanisms with convolutional long short-term memory neural networks (ConvLSTM). The core concept involves accounting for among various while extracting temporal information from assigning appropriate weights different entities. Furthermore, introduce an interpolation method temperature salinity based on KNN algorithm enhance dataset resolution. Experimental results indicate provides precise predictions speed. Specifically, relative measured data, it achieved root mean square error (RMSE) approximately 0.57 m/s absolute (MAE) about 0.29 m/s. Additionally, when compared single-dimensional analysis, incorporating scale considerations yielded superior predictive performance.

Язык: Английский

Attention-ConvNet Network for Ocean Front Prediction via Remote Sensing SST Images DOI Creative Commons
Yuting Yang, Xin Sun, Junyu Dong

и другие.

IEEE Transactions on Geoscience and Remote Sensing, Год журнала: 2024, Номер 62, С. 1 - 16

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

4
Leveraging ResUnet, oceanic and atmospheric data for accurate chlorophyll-a estimations in the South China Sea DOI Creative Commons
Weiwei Fang, Ao Li, Haoyu Jiang

и другие.

Frontiers in Marine Science, Год журнала: 2025, Номер 12

Опубликована: Март 3, 2025

Chlorophyll-a (Chl-a) plays a vital role in assessing environmental health and understanding the response of marine ecosystems to physical factors climate change. In situ sampling, remote sensing, moored buoys or floats are commonly employed methods for obtaining Chl-a science research. Although buoys, could provide accurate data, they limited by spatial temporal resolution. Remote sensing offers continuous broad coverage, while it is often hindered cloud cover South China Sea (SCS). This study discussed feasibility predictive model linking [e.g., wind field, surface currents, sea height (SSH), temperature (SST)] with SCS based on ResUnet. The ResUnet architecture performs well capturing non-linear relationships between variables, achieving prediction accuracy exceeding 90%. results indicate that (1) combination oceanic dynamical meteorological data effectively estimate deep learning methods; (2) SST reproduces northern SCS, adding currents SSH improves performance southern SCS; (3) With addition SSH, captures high patches induced eddies. research presents viable method estimating concentrations regions where highly correlated dynamic factors, using comprehensive atmospheric data.

Язык: Английский

Процитировано

0
A Multi-Spatial Scale Ocean Sound Speed Prediction Method Based on Deep Learning DOI Creative Commons
Yü Liu, Benjun Ma, Zhiliang Qin

и другие.

Journal of Marine Science and Engineering, Год журнала: 2024, Номер 12(11), С. 1943 - 1943

Опубликована: Окт. 31, 2024

As sound speed is a fundamental parameter of ocean acoustic characteristics, its prediction central focus underwater acoustics research. Traditional numerical and statistical forecasting methods often exhibit suboptimal performance under complex conditions, whereas deep learning approaches demonstrate promising results. However, these methodologies fall short in adequately addressing multi-spatial coupling effects spatiotemporal weighting, particularly scenarios characterized by limited data availability. To investigate the interactions across multiple spatial scales to achieve accurate predictions, we propose STA-ConvLSTM framework that integrates attention mechanisms with convolutional long short-term memory neural networks (ConvLSTM). The core concept involves accounting for among various while extracting temporal information from assigning appropriate weights different entities. Furthermore, introduce an interpolation method temperature salinity based on KNN algorithm enhance dataset resolution. Experimental results indicate provides precise predictions speed. Specifically, relative measured data, it achieved root mean square error (RMSE) approximately 0.57 m/s absolute (MAE) about 0.29 m/s. Additionally, when compared single-dimensional analysis, incorporating scale considerations yielded superior predictive performance.

Язык: Английский

Процитировано

0