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In this paper, a time-series simulation prediction study of pollen concentration in Shunyi District, Beijing, China, for the period of 2018~2024 was carried out based on machine learning and time-series algorithms. The study integrates traditional statistical models, machine learning models and deep learning models, and introduces 36 meteorological elements and their variables as covariates to improve the prediction accuracy. The results show that AutoGluon-TimeSeries (AG-TS) significantly reduces the risk of overfitting through model integration, and performs well in short-term prediction (1day), with DLinear model having the best overall performance with R=0.68 and RMSE=206.5. In contrast, some models overestimated or underestimated the predictions. It was found that the pollen concentration showed double peaks in spring (March~May) and fall (August~October), and the correlation with meteorological variables reflected the complex effects of meteorological conditions and vegetation phenology. In addition, the modeling performance declined with the extension of prediction time, e.g., the R-value of DLinear decreased by 18% and the RMSE increased by 15% in the 3-day prediction. By eliminating the inefficient models and optimizing the remaining models, the direction of model optimization and scientific support for pollen allergy early warning were provided.

, authors=Tian-feng DENG¹, Zi-ming LI^{2, *}, Shao-bo ZHANG^{1, **}, Zhao-bin SUN³, authorsList=Tian-feng DENG, Zi-ming LI, Shao-bo ZHANG, Zhao-bin SUN, authorCompany=null, correspAuthors=Zi-ming LI, Shao-bo 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=1314918418696995260, articleId=1314918418206261690, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=基于机器学习和时间序列算法融合的北京地区花粉浓度预测技术研究, columnId=1234106388364981004, journalTitle=中国环境科学, columnName=大气污染与控制, runingTitle=null, highlight=null, articleAbstract=

基于机器学习和时间序列算法,对北京市顺义区2018~2024年的花粉浓度进行了时间序列模拟预测研究.研究整合了传统统计模型、机器学习模型和深度学习模型,并引入36种气象要素及其变量作为协变量以提升预测精度.结果显示,AutoGluon-TimeSeries（AG-TS）通过模型集成显著降低了过拟合风险,在短期预测（1d）中表现优异,其中DLinear模型以R=0.68和RMSE=206.5的综合性能最佳.相比之下,部分模型在预测时有高估或低估现象.研究发现,花粉浓度呈现春季（3~5月）和秋季（8~10月）双峰特征,与气象变量的相关性反映了气象条件和植被物候的复杂影响.此外,模型性能随预测时间延长衰减,如DLinear在3d预测中R值下降18%,RMSE上升15%.通过剔除低效模型并优化剩余模型,为花粉过敏预警提供了模型优化的方向和科学支持.

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邓田丰（1998-），男，天津人，硕士，主要从事气象健康方向研究．1102254630@qq.com.

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邓田丰（1998-），男，天津人，硕士，主要从事气象健康方向研究．1102254630@qq.com.

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