Quantitative analysis and prediction of the sound field convergence zone in mesoscale eddy environment based on data mining methods

作     者：Ming Li Yuhang Liu Yiyuan Sun Kefeng Liu Ming Li;Yuhang Liu;Yiyuan Sun;Kefeng Liu

作者机构：College of Advanced Interdisciplinary StudiesNational University of Defense TechnologyNanjing 211101China Jiangsu Ocean UniversityLianyungang 222061China College of Horticulture Forestry SciencesHuazhong Agricultural UniversityWuhan 430070China 

出 版 物：《Acta Oceanologica Sinica》 (海洋学报（英文版）)

年 卷 期：2024年第43卷第5期

页      面：110-120页

核心收录：

学科分类：082403[工学-水声工程] 08[工学] 0824[工学-船舶与海洋工程] 

基　　金：The National Natural Science Foundation of China under contract Nos 41875061 and 41775165 

主　　题：convergence zone mesoscale eddy statistic analysis quantitative prediction machine learning 

摘      要：The mesoscale eddy(ME)has a significant influence on the convergence effect in deep-sea acoustic *** paper use statistical approaches to express quantitative relationships between the ME conditions and convergence zone(CZ)*** on the Gaussian vortex model,we construct various sound propagation scenarios under different eddy conditions,and carry out sound propagation experiments to obtain simulation *** a large number of samples,we first adopt the unified regression to set up analytic relationships between eddy conditions and CZ *** sensitivity of eddy indicators to the CZ is quantitatively ***,we adopt the machine learning(ML)algorithms to establish prediction models of CZ parameters by exploring the nonlinear relationships between multiple ME indicators and CZ *** the research,we can express the influence of ME on the CZ quantitatively,and achieve the rapid prediction of CZ parameters in ocean *** prediction accuracy(R)of the CZ distance(mean R:0.9815)is obviously better than that of the CZ width(mean R:0.8728).Among the three ML algorithms,Gradient Boosting Decision Tree has the best prediction ability(root mean square error(RMSE):0.136),followed by Random Forest(RMSE:0.441)and Extreme Learning Machine(RMSE:0.518).