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This study constructed a data-driven RF-LSTM model to predict Total Nitrogen (TN) concentrations in the Haihe River, based on monitoring data from 69 water quality stations collected between 2021 and 2024. By coupling the Random Forest (RF) algorithm with a Long Short-Term Memory (LSTM) neural network, the model also explored the spatiotemporal variation and key influencing factors of TN. Results showed that TN concentrations were relatively high in 2021 and lower in 2022, with annual averages ranging from 4.97 to 5.22mg/L. Across the five watersheds, TN levels displayed a U-shaped seasonal pattern, peaking in January and February and reaching the lowest levels in August. Temperature and dissolved oxygen were identified as the main influencing factors. TN concentration showed a highly significant negative correlation with temperature and a highly significant positive correlation with dissolved oxygen. The RF-LSTM model outperformed the traditional standalone LSTM model in prediction accuracy (R²:0.830~0.939, R²:0.818~0.931) and demonstrated stronger generalization performance.

, authors=Hao WANG¹, Kai-lu GAO¹, Gao-fei YIN¹, Xue-ying SUN², Yi-hang LIU¹, Xu-lin ZHANG¹, Feng WANG³, Meng-yu ZHAI⁴, Jia-bao WANG¹, Zhao-hui LI¹, Hong-da WEN¹, Wen-chao LI^{1, *}, authorsList=Hao WANG, Kai-lu GAO, Gao-fei YIN, Xue-ying SUN, Yi-hang LIU, Xu-lin ZHANG, Feng WANG, Meng-yu ZHAI, Jia-bao WANG, Zhao-hui LI, Hong-da WEN, Wen-chao LI, authorCompany=null, correspAuthors=Wen-chao LI, 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=1314917585641108231, articleId=1314917585112625925, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=海河流域河流氮素变化趋势及关键因子识别, columnId=1234106388268503686, journalTitle=中国环境科学, columnName=环境生态, runingTitle=null, highlight=null, articleAbstract=

基于2021~2024年海河流域69个水质监测站的监测数据，通过耦合随机森林模型（RF）和长短时记忆神经网络（LSTM）构建了TN浓度预测的RF-LSTM数据驱动模型，并探究了TN浓度的时空变化规律及主要影响因素．结果表明：2021年TN浓度较高，2022年TN浓度较低，年均值变化在4.97~5.22mg/L；5个子流域TN浓度年内呈U型的季节变化特征，TN浓度最高值出现在1月和2月，最低值出现在8月；水温和溶解氧浓度是影响海河流域水体TN浓度的关键因子，水温与TN浓度呈极显著负相关，溶解氧浓度与TN浓度呈极显著正相关；RF-LSTM数据驱动模型的预测效果（R²：0.830~0.939）优于传统单一LSTM（R²：0.818~0.931）模型，且RF-LSTM数据驱动模型具有更强的泛化性能．

, authors=汪昊¹, 高凯璐¹, 尹高飞¹, 孙学莹², 刘一航¹, 张旭琳¹, 王凤³, 翟梦瑜⁴, 王佳宝¹, 李昭慧¹, 文宏达¹, 李文超^{1, *}, authorsList=汪昊, 高凯璐, 尹高飞, 孙学莹, 刘一航, 张旭琳, 王凤, 翟梦瑜, 王佳宝, 李昭慧, 文宏达, 李文超, authorCompany=null, correspAuthors=李文超, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=vdK1yUidZGBmqlLSLvu76g==, magXml=x7O2mOuWEFIFzUhz8ESwuQ==, pdfUrl=null, pdf=F6t/zfAL7HCtNHUm2KfXSw==, pdfFileSize=1457890, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=60Nsh0a+/81PH3x5jwrXUA==, mapNumber=null, fund=null)}, authors=[Author(id=1314917586333168405, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1314917585112625925, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=wangh04282014@163.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1314917586425443095, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1314917585112625925, authorId=1314917586333168405, language=EN, stringName=Hao WANG, firstName=Hao, middleName=null, lastName=WANG, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.Key Laboratory of Ecological Environment of Farmland in Hebei, State Key Laboratory of North China Crop Improvement and Regulation, College of Resources and Environmental Sciences, Hebei Agricultural University, Baoding 071000, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1314917586505134872, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1314917585112625925, authorId=1314917586333168405, language=CN, stringName=汪昊, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.河北农业大学资源与环境科学学院，省部共建华北作物改良与调控国家重点实验室，河北省农田生态环境重点实验室，河北 保定 071000, bio={"content":"

汪昊（1996-），男，辽宁锦州人，河北农业大学硕士研究生，主要从事农业面源污染研究．wangh04282014@163.com.

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汪昊（1996-），男，辽宁锦州人，河北农业大学硕士研究生，主要从事农业面源污染研究．wangh04282014@163.com.

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