Machine learning–based feature prediction of convergence zones in ocean front environments
The convergence zone holds significant importance in deep-sea underwater acoustic propagation, playing a pivotal role in remote underwater acoustic detection and communication. Despite the adaptability and predictive power of machine learning, its practical application in predicting the convergence...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-01-01
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| Series: | Frontiers in Marine Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2024.1337234/full |
| _version_ | 1797347121596203008 |
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| author | Weishuai Xu Lei Zhang Hua Wang |
| author_facet | Weishuai Xu Lei Zhang Hua Wang |
| author_sort | Weishuai Xu |
| collection | DOAJ |
| description | The convergence zone holds significant importance in deep-sea underwater acoustic propagation, playing a pivotal role in remote underwater acoustic detection and communication. Despite the adaptability and predictive power of machine learning, its practical application in predicting the convergence zone remains largely unexplored. This study aimed to address this gap by developing a high-resolution ocean front-based model for convergence zone prediction. Out of 24 machine learning algorithms tested through K-fold cross-validation, the multilayer perceptron–random forest hybrid demonstrated the highest accuracy, showing its superiority in predicting the convergence zone within a complex ocean front environment. The research findings emphasized the substantial impact of ocean fronts on the convergence zone’s location concerning the sound source. Specifically, they highlighted that in relatively cold (or warm) water, the intensity of the ocean front significantly influences the proximity (or distance) of the convergence zone to the sound source. Furthermore, among the input features, the turning depth emerged as a crucial determinant, contributing more than 25% to the model’s effectiveness in predicting the convergence zone’s distance. The model achieved an accuracy of 82.43% in predicting the convergence zone’s distance with an error of less than 1 km. Additionally, it attained a 77.1% accuracy in predicting the convergence zone’s width within a similar error range. Notably, this prediction model exhibits strong performance and generalizability, capable of discerning evolving trends in new datasets when cross-validated using in situ observation data and information from diverse sea areas. |
| first_indexed | 2024-03-08T11:43:06Z |
| format | Article |
| id | doaj.art-021eb83f2a0741169b03b576218677c8 |
| institution | Directory Open Access Journal |
| issn | 2296-7745 |
| language | English |
| last_indexed | 2024-03-08T11:43:06Z |
| publishDate | 2024-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj.art-021eb83f2a0741169b03b576218677c82024-01-25T04:42:29ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452024-01-011110.3389/fmars.2024.13372341337234Machine learning–based feature prediction of convergence zones in ocean front environmentsWeishuai Xu0Lei Zhang1Hua Wang2No.5 Student Team, Dalian Naval Academy, Dalian, Liaoning, ChinaDepartment of Military Oceanography and Hydrography and Cartography, Dalian Naval Academy, Dalian, Liaoning, ChinaDepartment of Military Oceanography and Hydrography and Cartography, Dalian Naval Academy, Dalian, Liaoning, ChinaThe convergence zone holds significant importance in deep-sea underwater acoustic propagation, playing a pivotal role in remote underwater acoustic detection and communication. Despite the adaptability and predictive power of machine learning, its practical application in predicting the convergence zone remains largely unexplored. This study aimed to address this gap by developing a high-resolution ocean front-based model for convergence zone prediction. Out of 24 machine learning algorithms tested through K-fold cross-validation, the multilayer perceptron–random forest hybrid demonstrated the highest accuracy, showing its superiority in predicting the convergence zone within a complex ocean front environment. The research findings emphasized the substantial impact of ocean fronts on the convergence zone’s location concerning the sound source. Specifically, they highlighted that in relatively cold (or warm) water, the intensity of the ocean front significantly influences the proximity (or distance) of the convergence zone to the sound source. Furthermore, among the input features, the turning depth emerged as a crucial determinant, contributing more than 25% to the model’s effectiveness in predicting the convergence zone’s distance. The model achieved an accuracy of 82.43% in predicting the convergence zone’s distance with an error of less than 1 km. Additionally, it attained a 77.1% accuracy in predicting the convergence zone’s width within a similar error range. Notably, this prediction model exhibits strong performance and generalizability, capable of discerning evolving trends in new datasets when cross-validated using in situ observation data and information from diverse sea areas.https://www.frontiersin.org/articles/10.3389/fmars.2024.1337234/fullconvergence zonemachine learningKuroshio extension frontenvironmental feature extractionmultiple regression prediction |
| spellingShingle | Weishuai Xu Lei Zhang Hua Wang Machine learning–based feature prediction of convergence zones in ocean front environments Frontiers in Marine Science convergence zone machine learning Kuroshio extension front environmental feature extraction multiple regression prediction |
| title | Machine learning–based feature prediction of convergence zones in ocean front environments |
| title_full | Machine learning–based feature prediction of convergence zones in ocean front environments |
| title_fullStr | Machine learning–based feature prediction of convergence zones in ocean front environments |
| title_full_unstemmed | Machine learning–based feature prediction of convergence zones in ocean front environments |
| title_short | Machine learning–based feature prediction of convergence zones in ocean front environments |
| title_sort | machine learning based feature prediction of convergence zones in ocean front environments |
| topic | convergence zone machine learning Kuroshio extension front environmental feature extraction multiple regression prediction |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2024.1337234/full |
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