Article(id=1314917646101999820, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314917567911788907, 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=1740067200000, receivedDateStr=2025-02-21, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1791430396818, onlineDateStr=2026-10-08, pubDate=1758297600000, pubDateStr=2025-09-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791430396818, onlineIssueDateStr=2026-10-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791430396818, creator=13701087609, updateTime=1791430396818, updator=13701087609, issue=Issue{id=1314917567911788907, tenantId=1146029695717560320, journalId=1234093305789726721, year='2025', volume='45', issue='9', pageStart='4737', pageEnd='5328', issueExtLink='null', onlineDate='null', pubDate='1758297600000', pubDateStr='2025-09-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1791430378177, creator='13701087609', updateTime=1791431890195, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1314923910001418489, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314917567911788907, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1314923910001418490, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314917567911788907, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=4986, endPage=4996, ext={EN=ArticleExt(id=1314917647339319501, articleId=1314917646101999820, tenantId=1146029695717560320, journalId=1234093305789726721, language=EN, title=Machine learning models for fine characterization of landfill leakage and uncertainty analysis, columnId=1240689598498656298, journalTitle=China Environmental Science, columnName=Solid Waste, runingTitle=null, highlight=null, articleAbstract=

This study addresses the limitations of traditional landfill groundwater pollution risk prediction methods by integrating high-performance simulation with uncertainty analysis to more accurately capture the effects of concealed leakage. The limitations of high computational load and low efficiency were addressed by combining a Particle Swarm Optimization (PSO) algorithm with an Extreme Gradient Boosting (XGBoost) model. By learning from multiple samples generated by detailed simulation models, a surrogate model was established to improve prediction efficiency. Additionally, an improved sampling method was proposed to reduce the reliance of PSO-XGBoost on sample size. The results indicate that the machine learning surrogate model developed in this study better captures the impact of parameter uncertainty compared to traditional analytical-Monte Carlo methods such as Landsim. The predicted pollution concentration range is 1.36 times greater than that of conventional models. The PSO-XGBoost surrogate model, combined with the improved sampling method, outperforms other machine learning models in prediction accuracy (R²= 0.99) and stability, while reducing the computational time for model training by 70%, significantly improving the efficiency of uncertainty analysis. The application research based on this model shows that the probability of the characteristic pollutant COD exceeding the standard in the 10th year reaches 56.43%, and the possible distance of exceeding the standard is 54meters, indicating that there is a certain pollution risk in the study area. These findings provide an efficient and precise scientific method for analyzing landfill leakage risks and uncertainties and offer valuable references for further related research.

, authors=Chang-xin NAI^{1, 2}, Hui-min ZHANG^{1, 2}, Xiao-chen SUN^{1, *}, Ya XU^{2, 3, *}, Jing-cai LIU², Yu-qiang LIU², authorsList=Chang-xin NAI, Hui-min ZHANG, Xiao-chen SUN, Ya XU, Jing-cai LIU, Yu-qiang LIU, authorCompany=null, correspAuthors=Xiao-chen SUN, Ya XU, 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=1314917647800692942, articleId=1314917646101999820, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=机器学习模型精细表征填埋场渗漏风险及其不确定性, columnId=1240689598737731645, journalTitle=中国环境科学, columnName=固体废物, runingTitle=null, highlight=null, articleAbstract=

针对传统基于解析解与Monte Carlo耦合的填埋场地下水污染风险预测方法难以准确刻画渗漏不确定性的问题，提出了一种高性能仿真模拟与不确定性分析耦合的填埋场渗漏风险及其不确定性表征技术．为解决传统不确定性分析框架下，高仿真模拟数值模型计算负荷大，不确定性分析效率低的问题，引入粒子群优化算法（PSO）与极限梯度提升算法（XGBoost）模型，通过学习精细模拟模型计算获得的多组样本，建立替代模型来预测；同时，为了解决PSO-XGBoost对抽样数量的过度依赖，提出了改进的抽样方法．实验结果表明，与以Landsim为代表的解析解-monte carlo耦合算法相比，本研究构建基于数值模拟模型样本的机器学习替代模型更好的捕捉了参数不确定性对结果的影响，预测的污染浓度分布区间平均为传统模型的1.36倍；基于PSO-XGBoost的替代模型及改进的抽样方法，相比于其他机器学习模型，预测精度（R²=0.99）和稳定性更好，且模型训练阶段计算时间减少70%，大幅提升了不确定性分析的效率．基于该模型的应用研究表明，第10a特征污染物COD超标的概率达到56.43%，存在超标可能的距离为54m，表明研究区存在一定污染风险．研究成果为填埋场渗漏污染风险及不确定性分析提供了高效、精准的科学方法，并为进一步开展相关研究提供了借鉴．

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能昌信（1965-），男，山东临沂人，教授，博士，主要从事环境监测技术研究．发表论文100余篇．naicx@126.com.

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能昌信（1965-），男，山东临沂人，教授，博士，主要从事环境监测技术研究．发表论文100余篇．naicx@126.com.

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