面向深度学习的空气质量预测研究进展  

Research Progress of Air Quality Prediction Based on Deep Learning

作　　者：赵小明 顾珂铭[1] 张石清 ZHAO Xiao-Ming;GU Ke-Ming;ZHANG Shi-Qing(School of Informatics Science and Technology,Zhejiang Sci-Tech University,Hangzhou 310018,China;Institute of Intelligent Information Processing,Taizhou University,Taizhou 317000,China)

机构地区：[1]浙江理工大学信息学院,杭州310018 [2]台州学院智能信息处理研究所,台州317000

出　　处：《计算机系统应用》2022年第11期49-59,共11页Computer Systems & Applications

基　　金：国家自然科学基金(61976149);浙江省自然科学基金(LZ20F020002)

摘　　要：空气污染是影响公共卫生的重要因素,空气质量预测是空气污染预警的关键,是近年来环境学、统计学、计算机科学等领域中的热点研究课题.本文综述了空气质量预测方法的研究现状与进展,尤其对近年来新发展起来的深度学习方法在空气质量预测方面的应用进行了系统分析与总结.首先,介绍了空气质量预测方法的演变历程和空气污染数据集.然后,阐述了传统空气质量预测方法.随后,从时间信息、时空信息、注意力机制等角度出发,重点分析和比较了现有面向深度学习的空气质量预测方法的进展.最后,对空气质量预测方法的未来发展趋势进行了总结与展望.Air pollution is an important factor affecting public health,and air quality prediction is the key to air pollution early warning and a hot research topic in the fields of environmental science,statistics,and computer science in recent years.This study reviews the research status and progress of air quality prediction methods,with a special focus on the systematical analysis and summarization of the applications of the newly-emerged deep learning methods in recent years in air quality prediction.Specifically,the evolution process of air quality prediction methods and air pollution datasets are outlined.After the traditional air quality prediction methods are described,the progress of existing deep learning-based air quality prediction methods is analyzed and compared in detail from the perspectives of temporal information,temporalspatial information,and attention mechanisms.Finally,the development trend of air quality prediction methods is summarized and predicted.

关 键 词：空气污染 空气质量预测 深度学习 注意力机制 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] X51[自动化与计算机技术—控制科学与工程]