A Multi-Spatial Scale Ocean Sound Speed Prediction Method Based on Deep Learning
As sound speed is a fundamental parameter of ocean acoustic characteristics, its prediction is a central focus of underwater acoustics research. Traditional numerical and statistical forecasting methods often exhibit suboptimal performance under complex conditions, whereas deep learning approaches d...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-10-01
|
| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/12/11/1943 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846153280730693632 |
|---|---|
| author | Yu Liu Benjun Ma Zhiliang Qin Cheng Wang Chao Guo Siyu Yang Jixiang Zhao Yimeng Cai Mingzhe Li |
| author_facet | Yu Liu Benjun Ma Zhiliang Qin Cheng Wang Chao Guo Siyu Yang Jixiang Zhao Yimeng Cai Mingzhe Li |
| author_sort | Yu Liu |
| collection | DOAJ |
| description | As sound speed is a fundamental parameter of ocean acoustic characteristics, its prediction is a central focus of 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 and spatiotemporal weighting, particularly in scenarios characterized by limited data availability. To investigate the interactions across multiple spatial scales and to achieve accurate predictions, we propose the STA-ConvLSTM framework that integrates spatiotemporal attention mechanisms with convolutional long short-term memory neural networks (ConvLSTM). The core concept involves accounting for the coupling effects among various spatial scales while extracting temporal and spatial information from the data and assigning appropriate weights to different spatiotemporal entities. Furthermore, we introduce an interpolation method for ocean temperature and salinity data based on the KNN algorithm to enhance dataset resolution. Experimental results indicate that STA-ConvLSTM provides precise predictions of sound speed. Specifically, relative to the measured data, it achieved a root mean square error (RMSE) of approximately 0.57 m/s and a mean absolute error (MAE) of about 0.29 m/s. Additionally, when compared to single-dimensional spatial analysis, incorporating multi-spatial scale considerations yielded superior predictive performance. |
| format | Article |
| id | doaj-art-450705aec81a412abdc35d25b54a0749 |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-450705aec81a412abdc35d25b54a07492024-11-26T18:08:01ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-10-011211194310.3390/jmse12111943A Multi-Spatial Scale Ocean Sound Speed Prediction Method Based on Deep LearningYu Liu0Benjun Ma1Zhiliang Qin2Cheng Wang3Chao Guo4Siyu Yang5Jixiang Zhao6Yimeng Cai7Mingzhe Li8College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, ChinaAs sound speed is a fundamental parameter of ocean acoustic characteristics, its prediction is a central focus of 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 and spatiotemporal weighting, particularly in scenarios characterized by limited data availability. To investigate the interactions across multiple spatial scales and to achieve accurate predictions, we propose the STA-ConvLSTM framework that integrates spatiotemporal attention mechanisms with convolutional long short-term memory neural networks (ConvLSTM). The core concept involves accounting for the coupling effects among various spatial scales while extracting temporal and spatial information from the data and assigning appropriate weights to different spatiotemporal entities. Furthermore, we introduce an interpolation method for ocean temperature and salinity data based on the KNN algorithm to enhance dataset resolution. Experimental results indicate that STA-ConvLSTM provides precise predictions of sound speed. Specifically, relative to the measured data, it achieved a root mean square error (RMSE) of approximately 0.57 m/s and a mean absolute error (MAE) of about 0.29 m/s. Additionally, when compared to single-dimensional spatial analysis, incorporating multi-spatial scale considerations yielded superior predictive performance.https://www.mdpi.com/2077-1312/12/11/1943sound speed predictiondeep learninglong short-term memory neural networksspatiotemporal attention mechanisms |
| spellingShingle | Yu Liu Benjun Ma Zhiliang Qin Cheng Wang Chao Guo Siyu Yang Jixiang Zhao Yimeng Cai Mingzhe Li A Multi-Spatial Scale Ocean Sound Speed Prediction Method Based on Deep Learning Journal of Marine Science and Engineering sound speed prediction deep learning long short-term memory neural networks spatiotemporal attention mechanisms |
| title | A Multi-Spatial Scale Ocean Sound Speed Prediction Method Based on Deep Learning |
| title_full | A Multi-Spatial Scale Ocean Sound Speed Prediction Method Based on Deep Learning |
| title_fullStr | A Multi-Spatial Scale Ocean Sound Speed Prediction Method Based on Deep Learning |
| title_full_unstemmed | A Multi-Spatial Scale Ocean Sound Speed Prediction Method Based on Deep Learning |
| title_short | A Multi-Spatial Scale Ocean Sound Speed Prediction Method Based on Deep Learning |
| title_sort | multi spatial scale ocean sound speed prediction method based on deep learning |
| topic | sound speed prediction deep learning long short-term memory neural networks spatiotemporal attention mechanisms |
| url | https://www.mdpi.com/2077-1312/12/11/1943 |
| work_keys_str_mv | AT yuliu amultispatialscaleoceansoundspeedpredictionmethodbasedondeeplearning AT benjunma amultispatialscaleoceansoundspeedpredictionmethodbasedondeeplearning AT zhiliangqin amultispatialscaleoceansoundspeedpredictionmethodbasedondeeplearning AT chengwang amultispatialscaleoceansoundspeedpredictionmethodbasedondeeplearning AT chaoguo amultispatialscaleoceansoundspeedpredictionmethodbasedondeeplearning AT siyuyang amultispatialscaleoceansoundspeedpredictionmethodbasedondeeplearning AT jixiangzhao amultispatialscaleoceansoundspeedpredictionmethodbasedondeeplearning AT yimengcai amultispatialscaleoceansoundspeedpredictionmethodbasedondeeplearning AT mingzheli amultispatialscaleoceansoundspeedpredictionmethodbasedondeeplearning AT yuliu multispatialscaleoceansoundspeedpredictionmethodbasedondeeplearning AT benjunma multispatialscaleoceansoundspeedpredictionmethodbasedondeeplearning AT zhiliangqin multispatialscaleoceansoundspeedpredictionmethodbasedondeeplearning AT chengwang multispatialscaleoceansoundspeedpredictionmethodbasedondeeplearning AT chaoguo multispatialscaleoceansoundspeedpredictionmethodbasedondeeplearning AT siyuyang multispatialscaleoceansoundspeedpredictionmethodbasedondeeplearning AT jixiangzhao multispatialscaleoceansoundspeedpredictionmethodbasedondeeplearning AT yimengcai multispatialscaleoceansoundspeedpredictionmethodbasedondeeplearning AT mingzheli multispatialscaleoceansoundspeedpredictionmethodbasedondeeplearning |