Article(id=1314918604198474011, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314918450573701162, 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=1748102400000, receivedDateStr=2025-05-25, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1791430625247, onlineDateStr=2026-10-08, pubDate=1766160000000, pubDateStr=2025-12-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791430625247, onlineIssueDateStr=2026-10-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791430625247, creator=13701087609, updateTime=1791430625247, updator=13701087609, issue=Issue{id=1314918450573701162, tenantId=1146029695717560320, journalId=1234093305789726721, year='2025', volume='45', issue='12', pageStart='6513', pageEnd='7104', issueExtLink='null', onlineDate='null', pubDate='1766160000000', pubDateStr='2025-12-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1791430588620, creator='13701087609', updateTime=1791431575400, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1314922589479333965, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314918450573701162, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1314922589479333966, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314918450573701162, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=6793, endPage=6804, ext={EN=ArticleExt(id=1314918604408189212, articleId=1314918604198474011, tenantId=1146029695717560320, journalId=1234093305789726721, language=EN, title=Research on phosphorus load prediction of Baiyangdian Basin based on coupling model, columnId=1234106388083954308, journalTitle=China Environmental Science, columnName=Environmental Ecology, runingTitle=null, highlight=null, articleAbstract=

This study established an integrated SWAT-LSTM-MLP model to investigate phosphorus flux dynamics in the Baiyangdian Basin, China. The SWAT simulations revealed three key findings: (1) the average annual phosphorus input of the basin was 717.3t/a(57.6kg/km²·a) during 2019~2023, and the organic phosphorus (78.8%) was the main form, which showd an increasing trend in 2023; (2) the phosphorus flux showed significantly seasonal variation, the flood seasons (June-September) accounted for 85.57% of annual phosphorus loading (49.28kg/km²), which was significantly positive correlations (P<0.01) with precipitation, runoff, and sediment load; (3) for the spatial distribution, the phosphorous load of Fu-Cao-Pu River (79.3kg/km², 32.09%) and Baigou Diversion Canal (66.6kg/km², 26.96%) was higher than other region. Furthermore, in order to avoid the deficiency of the SWAT mobile in nonlinear varaitions, the LSTM-MLP hybrid model was applied to improve the accuracy of phosphorous load prediction. The model results showed that:(1) the LSTM-MLP hybrid model significantly improved prediction accuracy (NSE>0.9, RMSE<4kg/km²) compared to conventional LSTM (NSE>0.8, RMSE<7kg/km²), particularly in capturing peak values; (2) simulate analysis under different sediment reduction scenario demonstrated that the Baigou Diversion Canal under 60% sediment reduction scenario can reduce 35.29% of phosphorus load, which was the most effective regions among all subregions.

, authors=Yu-ang GAO¹, Shuang-jiang LI¹, Zi-shuai ZHENG¹, Hao-jia WANG¹, Xiao-ning LI¹, Zi-qiong LI¹, Zhao-nan DU¹, Jian-sheng CUI¹, Lu-lu ZHANG^{1, 2, *}, authorsList=Yu-ang GAO, Shuang-jiang LI, Zi-shuai ZHENG, Hao-jia WANG, Xiao-ning LI, Zi-qiong LI, Zhao-nan DU, Jian-sheng CUI, Lu-lu ZHANG, authorCompany=null, correspAuthors=Lu-lu ZHANG, 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=1314918604655653149, articleId=1314918604198474011, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=基于耦合模型的白洋淀流域磷负荷削减模拟, columnId=1234106388268503686, journalTitle=中国环境科学, columnName=环境生态, runingTitle=null, highlight=null, articleAbstract=

本研究选取白洋淀流域为研究区，通过构建SWAT（Soil and Water Assessment Tool）与LSTM-MLP（Long Short-Term Memory-Multilayer Perceptron）混合模型，模拟白洋淀流域磷通量时空分异规律，识别白洋淀流域磷通量的主要影响因素，预测不同泥沙削减情景下磷通量削减效果．总体而言，SWAT模型模拟结果表明：（1）2019~2023年白洋淀流域年均磷输入量为717.3t/a，单位面积磷负荷为57.6kg/（km²·a），其中有机磷占比78.8%，且磷通量呈逐年上升趋势，2023年尤为显著；（2）磷通量呈显著季节性变化，汛期（6~9月）负荷占全年磷通量的85.57%（49.28kg/km²），其与降雨量、径流量及泥沙量呈显著正相关（P<0.01）；（3）就不同区域而言，府-漕-瀑河区域（79.3kg/km²）和白沟引河区域（66.6kg/km²）磷负荷最高，其贡献率分别为32.09%和26.96%．此外，为弥补SWAT模型对非线性时序变化的不足，运用LSTM-MLP混合模型提升磷负荷预测精度，预测结果表明：（1）LSTM-MLP混合模型的NSE>0.9、RMSE<4kg/km²，均优于传统LSTM模型（NSE>0.8、RMSE<7kg/km²），能更精确捕捉峰值变化；（2）在模拟不同泥沙削减情景中，白沟引河区域在削减60%泥沙量的情况下，TP负荷削减率为35.29%，显著优于其他区域．

, authors=高宇昂¹, 李双江¹, 郑子帅¹, 王浩加¹, 李晓宁¹, 李子琼¹, 杜兆楠¹, 崔建升¹, 张璐璐^{1, 2, *}, authorsList=高宇昂, 李双江, 郑子帅, 王浩加, 李晓宁, 李子琼, 杜兆楠, 崔建升, 张璐璐, authorCompany=null, correspAuthors=张璐璐, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=+oMwdjU3yZJDEtx4iV45dg==, magXml=tzkRY8FjNxLEwdLcXZWokw==, pdfUrl=null, pdf=Iu0kr5cDlk+xEvAD5JnK5g==, pdfFileSize=3439325, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=/46phAW0uz9TJwxnKmK4SA==, mapNumber=null, fund=null)}, authors=[Author(id=1314918605012168997, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1314918604198474011, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=Gao220399@163.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1314918605108637991, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1314918604198474011, authorId=1314918605012168997, language=EN, stringName=Yu-ang GAO, firstName=Yu-ang, middleName=null, lastName=GAO, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.College of Environment Science and Engineering, Hebei University of Science the Technology, Shijiazhuang 050000, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1314918605184135464, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1314918604198474011, authorId=1314918605012168997, language=CN, stringName=高宇昂, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.河北科技大学环境科学与工程学院，河北 石家庄 050000, bio={"content":"

高宇昂（2001-），男，河北唐山人，硕士研究生，主要研究方向为流域污染物模型模拟．Gao220399@163.com.

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高宇昂（2001-），男，河北唐山人，硕士研究生，主要研究方向为流域污染物模型模拟．Gao220399@163.com.

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