学术报告：A Graph Attention Recurrent Neural Network Model for PM2.5 Prediction: A Case Study in China from 2015 to 2022

报告时间：5月16日（星期五）下午15:00-16:00

报告地点：沙河校区，学院1号楼102会议室

主持人：马凌飞 副教授

报告人：潘蕊，中央财经大学统计与数学学院，教授

报告摘要：Accurately predicting PM2.5 is a crucial task for protecting public health and making policy decisions. In the meanwhile, it is also a challenging task, given the complex spatio-temporal patterns of PM2.5 concentrations. Recently, the utilization of graph neural network (GNN) models has emerged as a promising approach, demonstrating significant advantages in capturing the spatial and temporal dependencies associated with PM2.5 concentrations. In this work, we collected a comprehensive dataset spanning 308 cities in China, encompassing data on seven pollutants as well as meteorological variables from January 2015 to September 2022. To effectively predict the PM2.5 concentrations, we propose a graph attention recurrent neural network (GARNN) model by taking into account both meteorological and geographical information. Extensive experiments validated the efficiency of the proposed GARNN model, revealing its superior performance compared to other existing methods in terms of predictive capabilities. This study contributes to advancing the understanding and prediction of PM2.5 concentrations, providing a valuable tool for addressing environmental challenges.

报告人简介：潘蕊，中央财经大学统计与数学学院教授、博士生导师，中央财经大学龙马学者青年学者。主要研究领域为网络结构数据的统计建模、时空数据的统计分析等。在Annals of Statistics、Journal of the American Statistical Association、Journal of Business & Economic Statistics等期刊发表论文30余篇。著有中文专著《数据思维实践》、《网络结构数据分析与应用》。主持国家自然科学基金项目、全国统计科学研究项目等。具有丰富的统计案例创作经验。曾获得中央财经大学青年教师教学基本功比赛二等奖，首届中国高校财经慕课联盟“同课异构”课程思政教学竞赛一等奖。

撰稿人：刘洁

审稿人：邓露