Parameterization Method of Wind Drift Factor Based on Deep Learning in the Oil Spill Model

作     者：YU Fangjie GU Feiyang ZHAO Yang HU Huimin ZHANG Xiaodong ZHUANG Zhiyuan CHEN Ge YU Fangjie;GU Feiyang;ZHAO Yang;HU Huimin;ZHANG Xiaodong;ZHUANG Zhiyuan;CHEN Ge

作者机构：College of Marine TechnologyFaculty of Information Science and EngineeringOcean University of ChinaQingdao 266100China Laboratory for Regional Oceanography and Numerical ModellingLaoshan LaboratoryQingdao 266100China College of Liberal ArtsJournalism and CommunicationOcean University of ChinaQingdao 266100China Qingdao Baifa Desalination CorporationQingdao 266100China 

出 版 物：《Journal of Ocean University of China》 (中国海洋大学学报（英文版）)

年 卷 期：2023年第22卷第6期

页      面：1505-1515页

核心收录：

学科分类：081505[工学-港口、海岸及近海工程] 0707[理学-海洋科学] 08[工学] 0815[工学-水利工程] 

基　　金：funded by the Social Science Foundation of Shandong(No.20CXWJ08) 

主　　题：oil spill prediction deep learning wind drift factor regional parameterization East China Sea 

摘      要：Oil spill prediction is critical for reducing the detrimental impact of oil spills on marine ecosystems,and the wind strong-ly influences the performance of oil spill ***,the wind drift factor is assumed to be constant or parameterized by linear regression and other methods in existing studies,which may limit the accuracy of the oil spill simulation.A parameterization method for wind drift factor(PMOWDF)based on deep learning,which can effectively extract the time-varying characteristics on a regional scale,is proposed in this *** method was adopted to forecast the oil spill in the East China *** discrepancies between predicted positions and actual measurement locations of the drifters are obtained using seasonal statistical *** reveal that PMOWDF can improve the accuracy of oil spill simulation compared with the traditional ***,the parameteriza-tion method is validated with satellite observations of the Sanchi oil spill in 2018.