[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122833-en":3,"doc-seo-122833-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122833,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine learning–based feature prediction of convergence zones in ocean front environments","Convergence zones play a critical role in deep-sea underwater acoustic propagation, yet practical prediction within ocean front environments remains underexplored despite machine learning’s capability. This research builds a high-resolution ocean front-based model to predict convergence zone location and geometry. Testing 24 algorithms with K-fold cross-validation, a multilayer perceptron–random forest hybrid achieves the best performance. Ocean front intensity strongly affects convergence zone distance, while turning depth contributes over 25%, reaching 82.43% distance accuracy (\u003C1 km error) and 77.1% width accuracy.","TYPE Original Research PUBLISHED 25 January 2024 DOI 10.3389/fmars.2024.1337234  \nOPEN ACCESS  \nEDITED BY  \nHaiyong Zheng,  \nOcean University of China, China  \nREVIEWED BY Feng Gao,  \nOcean University of China, China Tianyu Thang,  \nGuangdong Ocean University, China  \n*CORRESPONDENCE Lei Zhang  \n [stone333@tom.com](stone333@tom.com)  \nRECEIVED 14 November 2023  \nACCEPTED 10 January 2024  \nPUBLISHED 25 January 2024  \nCITATION  \nXu W, Zhang L and Wang H (2024)  \nMachine learning–based feature prediction of convergence zones in oceanfront environments.  \nFront. Mar. Sci. 11:1337234 .  \ndoi: 10.3389/fmars.2024.1337234  \nCOPYRIGHT  \n© 2024 Xu, Zhang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMachine learning–based feature prediction of convergence zones in ocean front environments  \nWeishuai Xu 1, Lei Zhang 2* and Hua Wang 2  \n1 No. 5 Student Team, Dalian Naval Academy, Dalian, Liaoning, China, 2 Department of Military  \nOceanography and Hydrography and Cartography, Dalian Naval Academy, Dalian, Liaoning, China  \nThe 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.  \nKEYWORDS  \nconvergence zone, machine learning, Kuroshio extension front, environmental feature extraction, multiple regression prediction  \n1 Introduction  \nIn typical deep-sea environments, when the source and receiver are at shallower depths than the channel axis, the sound line experiences inversion or reflection, oscillating away from and toward the channel axis. This phenomenon creates the convergence zone (CZ), marked by periodic high acoustic intensity dispersion, crucial for underwater target  \nFrontiers in Marine Science 01 [frontiersin.org](frontiersin.org)  \ndetection and long-range acoustic communication (Hanrahan, 1987) . The characteristics of the CZ, such as its location, gain, and energy distribution, are closely tied to the deep-sea acoustic velocity profile (Wu et al., 2023)., as mesoscale oceanographic phenomen","cbCaisGv7iXsgUuc","https://ap.wps.com/l/cbCaisGv7iXsgUuc","pdf",13338591,1,14,"English","en",105,"# Introduction\n## Deep-sea convergence zones and acoustic propagation\n## Influence of ocean fronts on CZ characteristics\n## Prior observational and modeling studies","[{\"question\":\"Why are convergence zones important in deep-sea underwater acoustics?\",\"answer\":\"Convergence zones cause periodic high acoustic intensity dispersion, supporting underwater target detection and long-range acoustic communication.\"},{\"question\":\"Which machine learning approach performed best in this study?\",\"answer\":\"A multilayer perceptron–random forest hybrid delivered the highest accuracy among 24 tested algorithms using K-fold cross-validation.\"},{\"question\":\"What factors most influence convergence zone distance and width according to the results?\",\"answer\":\"Ocean front intensity determines whether the convergence zone is closer or farther from the sound source, and turning depth is the key input feature. The model predicts distance with 82.43% accuracy (\\u003c1 km error) and width with 77.1% accuracy within a similar error range.\"}]","Machine learning–based feature prediction of convergence zones in ocean front environments | PDF",1785813155,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learningbased-feature-prediction-of-convergence-zones-in-ocean-front-environments","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learningbased-feature-prediction-of-convergence-zones-in-ocean-front-environments/122833/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why are convergence zones important in deep-sea underwater acoustics?","Question",{"text":76,"@type":77},"Convergence zones cause periodic high acoustic intensity dispersion, supporting underwater target detection and long-range acoustic communication.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning approach performed best in this study?",{"text":81,"@type":77},"A multilayer perceptron–random forest hybrid delivered the highest accuracy among 24 tested algorithms using K-fold cross-validation.",{"name":83,"@type":74,"acceptedAnswer":84},"What factors most influence convergence zone distance and width according to the results?",{"text":85,"@type":77},"Ocean front intensity determines whether the convergence zone is closer or farther from the sound source, and turning depth is the key input feature. The model predicts distance with 82.43% accuracy (\u003C1 km error) and width with 77.1% accuracy within a similar error range.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]