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Algal blooms are a major ecological and environmental issue in the tributary bays of the Three Gorges Reservoir. To identify the dominant environmental factors driving algal blooms in the Xiangxi Bay, this study utilized high-frequency monitoring data collected from August 9 to August 30, 2024, with chlorophyll-a (Chl.a) concentration as the bloom indicator. Three machine learning models—Random Forest (RF), Support Vector Regression (SVR), and Partial Least Squares Regression (PLS)—were applied to identify key driving factors. The results showed that algal blooms in Xiangxi Bay exhibited pronounced diurnal variation, with nighttime stable stratification and low disturbance favoring bloom formation, while daytime disturbances and high temperatures inhibited algal accumulation. Water velocity (WV) and air temperature (AT) were identified as immediate driving factors, with contribution rates of 27.1% and 18.1%, respectively. specific conductivity (SP) served as an important regulating factor (17.1%), while dissolved oxygen (DO), pH, and water temperature (WT) were secondary feedback factors. Moreover, the impacts of these factors on Algal blooms exhibited significant nonlinearity and lagged response characteristics.

, authors=Jing YANG¹, Zhong-yong YANG^{1, 2, *}, Yu-fang HE³, Li-wei XIONG¹, Yi-ling HU¹, Dao-bin JI^{1, 2}, authorsList=Jing YANG, Zhong-yong YANG, Yu-fang HE, Li-wei XIONG, Yi-ling HU, Dao-bin JI, authorCompany=null, correspAuthors=Zhong-yong YANG, 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=1314923029650567592, articleId=1314923028958507430, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=基于机器学习的香溪河库湾水华暴发主控因子识别及贡献, columnId=1234106388268503686, journalTitle=中国环境科学, columnName=环境生态, runingTitle=null, highlight=null, articleAbstract=

为明确香溪河库湾水华暴发的主控环境因子，基于2024年8月9~30日的高频监测数据，以叶绿素a（Chl.a）为水华表征指标，采用随机森林（RF）、支持向量回归（SVR）和偏最小二乘回归（PLS）模型识别关键驱动因子．结果表明，香溪河库湾水华过程具有显著的日内节律变化特征，夜间低扰动和热分层的稳定条件显著促进水华形成，而白昼扰动和高温环境则抑制水华积累．流速（WV）和气温（AT）是水华暴发的即时驱动因子，贡献率分别为27.1%和18.1%，电导率（SP）作为重要调节因子贡献率为17.1%，而溶解氧（DO）、pH值和水温（WT）则表现为次要的反馈性因子．此外，各因子在水华过程中表现出显著的非线性与滞后响应特征．

, authors=杨京¹, 杨忠勇^{1, 2, *}, 和玉芳³, 熊励伟¹, 胡怡玲¹, 纪道斌^{1, 2}, authorsList=杨京, 杨忠勇, 和玉芳, 熊励伟, 胡怡玲, 纪道斌, authorCompany=null, correspAuthors=杨忠勇, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=WtbZf+FLlwTKX4MsuHe6AA==, magXml=/XRyz9+6CFqIrq1Y7PLdwg==, pdfUrl=null, pdf=LNuZ008Wi7MKmyrigxFalg==, pdfFileSize=2827191, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=qXAclMsJOuzf9ndcJf9khA==, mapNumber=null, fund=null)}, authors=[Author(id=1314923030489428403, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1314923028958507430, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=Jing6171@qq.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1314923030573314485, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1314923028958507430, authorId=1314923030489428403, language=EN, stringName=Jing YANG, firstName=Jing, middleName=null, lastName=YANG, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.College of Hydraulic and Environmental Engineering, China Three Gorges University, Yichang 443002, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1314923030673977782, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1314923028958507430, authorId=1314923030489428403, language=CN, stringName=杨京, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.三峡大学水利与环境学院，湖北 宜昌 443002, bio={"content":"

杨京（2000-），男，四川南充人，三峡大学硕士研究生，主要从事湖库水华发生机制及其控制研究．发表论文3篇．Jing6171@qq.com.

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杨京（2000-），男，四川南充人，三峡大学硕士研究生，主要从事湖库水华发生机制及其控制研究．发表论文3篇．Jing6171@qq.com.

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