Article(id=1314918424359297067, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314918356222829028, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1743350400000, receivedDateStr=2025-03-31, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1791430582370, onlineDateStr=2026-10-08, pubDate=1763568000000, pubDateStr=2025-11-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791430582370, onlineIssueDateStr=2026-10-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791430582370, creator=13701087609, updateTime=1791430582370, updator=13701087609, issue=Issue{id=1314918356222829028, tenantId=1146029695717560320, journalId=1234093305789726721, year='2025', volume='45', issue='11', pageStart='5921', pageEnd='6512', issueExtLink='null', onlineDate='null', pubDate='1763568000000', pubDateStr='2025-11-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1791430566125, creator='13701087609', updateTime=1791431596967, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1314922679942078535, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314918356222829028, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1314922679942078536, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314918356222829028, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=6250, endPage=6261, ext={EN=ArticleExt(id=1314918426150264876, articleId=1314918424359297067, tenantId=1146029695717560320, journalId=1234093305789726721, language=EN, title=Multi feature dynamic weighted fusion model for predicting the trajectory of water-floating garbage, columnId=1234106388083954308, journalTitle=China Environmental Science, columnName=Environmental Ecology, runingTitle=null, highlight=null, articleAbstract=

To address the challenge of heightened uncertainty in water-floating garbage trajectory prediction caused by multiple influencing factors, a multi-feature dynamic weighted fusion model for water-floating garbage trajectory prediction was proposed. Firstly, a multi-factor feature dynamic weighted fusion layer was designed to thoroughly analyze the complex mapping relationships among various influencing factors. Secondly, a bidirectional spatial-temporal weighted attention network was introduced to perform weighted fusion of features extracted by BiLSTM, thereby capturing richer temporal-spatial characteristics of drift trajectories. Finally, a learnable nonlinear output layer was proposed to fully integrate information from diverse input factors and trajectory data, significantly enhancing the model's generalization capability and prediction reliability. Experimental results on the Yinggezui Reservoir trajectory dataset show that compared with baseline model, the R² index of the proposed method has improved by 0.1085, and the MAE, MAPE, and MBE indices have decreased by 0.0112, 3.715, and 0.000624. This method can fully integrate multiple factor data and achieve high-precision prediction of the trajectory of water-floating garbage, providing a reliable model for the salvage and management of water-floating garbage.

, authors=Jian-wei DENG¹, Long MA^{2, *}, Bai-jing WU², Guang-hui YAN², authorsList=Jian-wei DENG, Long MA, Bai-jing WU, Guang-hui YAN, authorCompany=null, correspAuthors=Long MA, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1314918426481614893, articleId=1314918424359297067, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=多特征动态加权融合的水漂垃圾轨迹预测模型, columnId=1234106388268503686, journalTitle=中国环境科学, columnName=环境生态, runingTitle=null, highlight=null, articleAbstract=

在水漂垃圾轨迹预测过程中,针对多重影响因素导致漂移轨迹不确定性加剧的问题,本文提出一种多特征动态加权融合的水漂垃圾轨迹预测模型.首先,设计了多因素特征动态加权融合层,深入解析各因素间复杂的映射关系;其次,提出了一种双向时空加权注意力网络,对BiLSTM提取的特征进行加权融合,获得了更加丰富的漂移轨迹时序特征;最后,为充分整合不同输入因素与轨迹数据信息,提出了可学习非线性输出层,增强了模型的泛化能力和可靠性.鹦鸽嘴水库漂移轨迹数据集上的结果表明,较于基准模型,本文所提方法的R²指标提升了0.1085,MAE、MAPE和MBE指标减少了0.0112、3.715和0.000624,该方法能够充分融合多因素数据,实现较高精度的水漂垃圾轨迹预测,为水漂垃圾的打捞治理提供了一种较为可靠的模型.

, authors=邓建伟¹, 马龙^{2, *}, 吴佰靖², 闫光辉², authorsList=邓建伟, 马龙, 吴佰靖, 闫光辉, authorCompany=null, correspAuthors=马龙, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=BCGmIT9CCtHyY9lGl0Pbdw==, magXml=uSxQtmRThlOassEIXcKH3A==, pdfUrl=null, pdf=KFyIOZ82Ag7NzDxy1lIY8A==, pdfFileSize=3017610, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=D1IodS/k933vXjJFdBnAhQ==, mapNumber=null, fund=null)}, authors=[Author(id=1314918426942988341, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1314918424359297067, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=djw415@163.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1314918427043651639, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1314918424359297067, authorId=1314918426942988341, language=EN, stringName=Jian-wei DENG, firstName=Jian-wei, middleName=null, lastName=DENG, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.Gansu Provincial Institute of Water Resources, Lanzhou 733070, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1314918427135926328, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1314918424359297067, authorId=1314918426942988341, language=CN, stringName=邓建伟, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.甘肃省水利科学研究院，甘肃 兰州 730070, bio={"content":"

邓建伟（1980-），男，河北唐山人，正高级工程师，硕士，主要从事水资源保护与节水灌溉技术方面研究．发表论文30余篇．djw415@163.com.

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邓建伟（1980-），男，河北唐山人，正高级工程师，硕士，主要从事水资源保护与节水灌溉技术方面研究．发表论文30余篇．djw415@163.com.

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