南京信息工程大学学报2024，Vol.16Issue(1)：56-65,10.DOI:10.13878/j.cnki.jnuist.20230421001
基于集成深度学习模型的空气质量指数预测
Air quality index prediction based on integrated deep learning model
摘要
Abstract
Air pollution seriously endangers the travel safety and health of residents.As a comprehensive indicator used to measure air quality condition,Air Quality Index(AQI)can alert the public to air quality and enable people to make more informed travel decisions.By predicting the change of air quality in advance,the government and envi-ronmental protection departments can take emergency measures to reduce air pollution.Here,we propose an integrat-ed deep learning model based on Convolutional Neural Network and Gated Recurrent Unit(CNN-GRU)for AQI prediction.The CNN is used to extract the spatial and temporal characteristics of air pollutants and AQI and com-plete the feature mapping,while the GRU to model the temporal relationship and complete the calculation and AQI efficiently.The daily average concentrations of six major air pollutants(PM2.5,PM10,SO2,CO,NO2,O3)in Beijing and Guangzhou during 2014-2022 are selected for example study,and the AQI is predicted using the CNN-GRU model.The results show that,compared with Multiverse-Optimized Generalized Regression Neural Network model(MVO-GRNN)and Genetic Algorithm-optimized BP neural network model(GA-BP),the proposed CNN-GRU model has the smallest prediction error for AQI.关键词
空气质量指数/卷积神经网络/门控循环单元/集成模型Key words
air quality index(AQI)/convolutional neural network(CNN)/gated recurrent unit(GRU)/integrated model分类
信息技术与安全科学引用本文复制引用
路凯丽,杨露,李涛..基于集成深度学习模型的空气质量指数预测[J].南京信息工程大学学报,2024,16(1):56-65,10.基金项目
资助项目国家社会科学基金青年项目(20CTJ008) （20CTJ008）
全国统计科学研究重点项目(2021LZ28) （2021LZ28）
陕西省自然科学基金项目(2022JQ-042) （2022JQ-042）
