Rapid Modeling of the Sound Speed Field in the South China Sea Based on a Comprehensive Optimal LM-BP Artificial Neural Network
Ocean sound speed is an essential foundation for marine scientific research and marine engineering applications. In this article, a model based on a comprehensive optimal back propagation artificial neural network model is developed. The Levenberg–Marquardt algorithm is used to optimize the model, a...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/9/5/488 |
| _version_ | 1797535537486102528 |
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| author | Jin Huang Yu Luo Jian Shi Xin Ma Qian-Qian Li Yan-Yi Li |
| author_facet | Jin Huang Yu Luo Jian Shi Xin Ma Qian-Qian Li Yan-Yi Li |
| author_sort | Jin Huang |
| collection | DOAJ |
| description | Ocean sound speed is an essential foundation for marine scientific research and marine engineering applications. In this article, a model based on a comprehensive optimal back propagation artificial neural network model is developed. The Levenberg–Marquardt algorithm is used to optimize the model, and the momentum term, normalization, and early termination method were used to predict the high precision marine sound speed profile. The sound speed profile was described by five indicators: date, time, latitude, longitude, and depth. The model used data from the CTD observation dataset of scientific investigation over the South China Sea (2009–2012) (108°–120° E, 6°–8° N), which includes comprehensive scientific investigation data from four voyages. The feasibility of modeling the sound speed field in the South China Sea is investigated. The proposed model uses the momentum term, normalization, and early termination in a traditional BP artificial neural network structure and mitigates issues with overtraining and difficulty when determining the BP neural network parameters. With the LM algorithm, a fast-modeling method for the sound field effectively achieves the precision requirement for sound speed prediction. Through the prediction and verification of the data from 2009 to 2012, the newly proposed optimized BP network model is shown to dramatically reduce the training time and improve precision compared to the traditional network model. Results showed that the root mean squared error decreased from 1.7903 m/s to 0.95732 m/s, and the training time decreased from 612.43 s to 4.231 s. Finally, the sound ray tracing simulations confirm that the model meets the accuracy requirements of acoustic sounding and verify the model’s feasibility for the real-time prediction of the vertical sound speed in saltwater bodies. |
| first_indexed | 2024-03-10T11:46:47Z |
| format | Article |
| id | doaj.art-f871537be17e4be5a65a101b40e4e874 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-10T11:46:47Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-f871537be17e4be5a65a101b40e4e8742023-11-21T18:04:42ZengMDPI AGJournal of Marine Science and Engineering2077-13122021-05-019548810.3390/jmse9050488Rapid Modeling of the Sound Speed Field in the South China Sea Based on a Comprehensive Optimal LM-BP Artificial Neural NetworkJin Huang0Yu Luo1Jian Shi2Xin Ma3Qian-Qian Li4Yan-Yi Li5College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266500, ChinaCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266500, ChinaCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266500, ChinaSchool of Information Science and Engineering, Shandong University, Jinan 250100, ChinaCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266500, ChinaCollege of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, ChinaOcean sound speed is an essential foundation for marine scientific research and marine engineering applications. In this article, a model based on a comprehensive optimal back propagation artificial neural network model is developed. The Levenberg–Marquardt algorithm is used to optimize the model, and the momentum term, normalization, and early termination method were used to predict the high precision marine sound speed profile. The sound speed profile was described by five indicators: date, time, latitude, longitude, and depth. The model used data from the CTD observation dataset of scientific investigation over the South China Sea (2009–2012) (108°–120° E, 6°–8° N), which includes comprehensive scientific investigation data from four voyages. The feasibility of modeling the sound speed field in the South China Sea is investigated. The proposed model uses the momentum term, normalization, and early termination in a traditional BP artificial neural network structure and mitigates issues with overtraining and difficulty when determining the BP neural network parameters. With the LM algorithm, a fast-modeling method for the sound field effectively achieves the precision requirement for sound speed prediction. Through the prediction and verification of the data from 2009 to 2012, the newly proposed optimized BP network model is shown to dramatically reduce the training time and improve precision compared to the traditional network model. Results showed that the root mean squared error decreased from 1.7903 m/s to 0.95732 m/s, and the training time decreased from 612.43 s to 4.231 s. Finally, the sound ray tracing simulations confirm that the model meets the accuracy requirements of acoustic sounding and verify the model’s feasibility for the real-time prediction of the vertical sound speed in saltwater bodies.https://www.mdpi.com/2077-1312/9/5/488sound speed profileCTD dataLevenberg–Marquardtmomentum termearly terminationback propagation neural network |
| spellingShingle | Jin Huang Yu Luo Jian Shi Xin Ma Qian-Qian Li Yan-Yi Li Rapid Modeling of the Sound Speed Field in the South China Sea Based on a Comprehensive Optimal LM-BP Artificial Neural Network Journal of Marine Science and Engineering sound speed profile CTD data Levenberg–Marquardt momentum term early termination back propagation neural network |
| title | Rapid Modeling of the Sound Speed Field in the South China Sea Based on a Comprehensive Optimal LM-BP Artificial Neural Network |
| title_full | Rapid Modeling of the Sound Speed Field in the South China Sea Based on a Comprehensive Optimal LM-BP Artificial Neural Network |
| title_fullStr | Rapid Modeling of the Sound Speed Field in the South China Sea Based on a Comprehensive Optimal LM-BP Artificial Neural Network |
| title_full_unstemmed | Rapid Modeling of the Sound Speed Field in the South China Sea Based on a Comprehensive Optimal LM-BP Artificial Neural Network |
| title_short | Rapid Modeling of the Sound Speed Field in the South China Sea Based on a Comprehensive Optimal LM-BP Artificial Neural Network |
| title_sort | rapid modeling of the sound speed field in the south china sea based on a comprehensive optimal lm bp artificial neural network |
| topic | sound speed profile CTD data Levenberg–Marquardt momentum term early termination back propagation neural network |
| url | https://www.mdpi.com/2077-1312/9/5/488 |
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