Please wait a minute...
浙江大学学报(理学版)  2021, Vol. 48 Issue (1): 74-83    DOI: 10.3785/j.issn.1008-9497.2021.01.011
地球科学     
融合图卷积神经网络和注意力机制的PM2.5小时浓度多步预测
傅颖颖1,2, 张丰1,2, 杜震洪1,2, 刘仁义1,2
1.浙江大学 浙江省资源与环境信息系统重点实验室,浙江 杭州 310028
2.浙江大学 地理信息科学研究所, 浙江 杭州 310027
Multi-step prediction of PM2.5 hourly concentration by fusing graph convolution neural network and attention mechanism
FU Yingying1,2, ZHANG Feng1,2, DU Zhenhong1,2, LIU Renyi1,2
1.Zhejiang Provincial Key Lab of GIS, Zhejiang University, Hangzhou 310028, China
2.Department of Geographic Information Science, Zhejiang University, Hangzhou 310027, China
 全文: PDF(3671 KB)   HTML  
摘要: PM2.5小时浓度多为单步预测。为实现PM2.5小时浓度的多步预测,基于“编码器-解码器”的序列-序列预测(Seq2Seq)模型,集合图卷积神经网络提取非欧式空间数据特征的能力以及注意力机制自适应关注特征的能力,提出了融合图卷积神经网络和注意力机制的PM2.5小时浓度多步预测(GCN_Attention_Seq2Seq)模型。并与Seq2Seq模型和使用了图卷积神经网络、未使用注意力机制的GCN_Seq2Seq模型进行了对照,以2015—2016年北京市22个空气质量监测站点的空气质量数据为样本进行实例验证,结果表明,Seq2Seq模型和图卷积神经网络(GCN)可对PM2.5小时浓度数据的时空依赖进行有效建模,注意力机制有助于减缓多步预测中的预测精度衰减,提升PM2.5小时浓度多步预测的精度。GCN_Attention_Seq2Seq模型可有效应用于多种长度的PM2.5浓度预测窗口。
关键词: 图卷积深度学习注意力机制PM2.5小时浓度多步预测    
Abstract: The current studies about PM2.5 hourly concentration prediction are mostly on single-step prediction.In order to achieve accurate prediction of PM2.5 hourly concentration at multiple moments in a single prediction task,this article proposes a multi-step prediction model of PM2.5 hourly concentration based on graph convolution neural network and attention mechanism,which is named GCN_Attention_Seq2Seq.The model based on Seq2Seq is able to extract the features of non-euclidean spatial data meantime pays attention to features adaptively.We take air quality data of 22 monitoring stations in Beijing from January 1st,2015 to December 29th,2016 as samples and compare GCN_Attention_Seq2Seq with GCN_Seq2Seq and Seq2Seq model. Results show that Seq2Seq and GCN can model spatio-temporal dependence effectively and the attention mechanism is helpful to improve the prediction accuracy and slow down the prediction accuracy decline in multi-step prediction,it indicates that the GCN_Attention_Seq2Seq model can be effectively applied to multi-step prediction of PM2.5 concentration.
Key words: multi-step prediction of PM2.5 hourly concentration    deep learning    graph convolution    attention mechanism
收稿日期: 2019-10-17 出版日期: 2021-01-20
CLC:  P208  
基金资助: 国家重点研发计划项目(2018YFB0505000);国家自然科学基金资助项目(41871287).
通讯作者: ORCID:http://orcid.org/0000-0003-1475-8480,E-mail:zfcarnation@zju.edu.cn.     E-mail: zfcarnation@zju.edu.cn
作者简介: 傅颖颖(1995—),ORCID:http://orcid.org/0000-0002-2543-1558,女,硕士研究生,主要从事时空大数据挖掘研;
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
傅颖颖
张丰
杜震洪
刘仁义

引用本文:

傅颖颖, 张丰, 杜震洪, 刘仁义. 融合图卷积神经网络和注意力机制的PM2.5小时浓度多步预测[J]. 浙江大学学报(理学版), 2021, 48(1): 74-83.

FU Yingying, ZHANG Feng, DU Zhenhong, LIU Renyi. Multi-step prediction of PM2.5 hourly concentration by fusing graph convolution neural network and attention mechanism. Journal of Zhejiang University (Science Edition), 2021, 48(1): 74-83.

链接本文:

https://www.zjujournals.com/sci/CN/10.3785/j.issn.1008-9497.2021.01.011        https://www.zjujournals.com/sci/CN/Y2021/V48/I1/74

1 王平利,戴春雷,张成江.城市大气中颗粒物的研究现状及健康效应[J].中国环境监测,2005,21(1):85-89. DOI:10.3969/j.issn.1002-6002.2005.01.025 WANG P L,DAI C L,ZHANG C J.The study progress in the research for the particular in city air and its effect on human health[J].Environmental Monitoring in China,2005,21(1):85-89. DOI:10.3969/j.issn.1002-6002.2005.01.025
2 SØRENSEN M,DANESHVAR B,HANSEN M,et al. Personal PM2.5 exposure and markers of oxidative stress in blood[J]. Environmental Health Perspectives,2003,111(2):161-165. DOI:10.1289/ehp.5646
3 ZHANG Y,BOCQUET M,MALLET V,et al. Real-time air quality forecasting,part I:History,techniques,and current status[J].Atmospheric Environment,2012,60(32):632-655. DOI:10.1016/j.atmosenv. 2012.06.031
4 徐文,黄泽纯,张倩宁.基于时空模型的PM2.5预测与插值[J].江苏师范大学学报(自然科学版),2016,34(3):70-75. DOI:10.3969/j.issn.2095-4298.2016. 03.016 XU W,HUANG Z C,ZHANG Q N.Prediction and interpolation of PM2.5 based on space-time model[J].Journal of Jiangsu Normal University(Natural Science Edition),2016,34(3):70-75. DOI:10.3969/j.issn.2095-4298.2016.03.016
5 范竣翔,李琦,朱亚杰,等.基于RNN的空气污染时空预测模型研究[J].测绘科学,2017,42(7):76-83. DOI:10.16251/j.cnki.1009-2307.2017.07.013 FAN J X,LI Q,ZHU Y J,et al.A Spatiotemporal prediction framework for air pollution based on deep RNN[J].Science of Surveying and Mapping,2017,42(7):76-83. DOI:10.16251/j.cnki.1009-2307. 2017.07.013
6 黄婕,张丰,杜震洪,等.基于RNN-CNN集成深度学习模型的PM2.5小时浓度预测[J].浙江大学学报(理学版),2019,46(3):370-379. DOI:10.3785/j.issn.1008-9497.2019.03.016 HUANG J,ZHANG F,DU Z H,et al.PM2.5 hourly concentration prediction based on RNN-CNN ensemble deep learning model[J].Journal of Zhejiang University(Science Edition),2019,46(3):370-379. DOI:10.3785/j.issn.1008-9497.2019.03.016
7 VASWANI A,SHAZEER N,PARMAR N,et al.Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook:Curran Associoctes Inc. 2017:5998-6008.
8 CHEN J,ZHU Q,SHI D,et al.A multi-step wind speed prediction model for multiple sites leveraging spatio-temporal correlation[J].Proceedings of the Chinese Society of Electrical Engineering,2019,39(7):2093-2105.
9 GUO J,YU Y B,YANG C Y.Multi-step prediction of traffic load with all-attention mechanism[J].Journal of Signal Processing,2019,35(5):758-767.
10 GOYENA R.A computer movie simulating urban growth in the detroit region[J].Journal of Chemical Information and Modeling,2019,53(9):1689-1699.
11 GOODFELLOW I,BENGIO Y,COURVILLE A.Deep Learning[M].Cambridge: The MIT Press,2016.
12 DEFFERRARD M,BRESSON X,VANDERGHEYNST P. Convolutional neural networks on graphs with fast localized spectral filtering[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems. Barcelona:NIPS' 16,2016:3844-3852.
13 YU B,YIN H T,ZHU Z X.Spatio-temporal graph convolutional networks:A deep learning framework for traffic forecasting[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence. Stockholm:IJCAI,2018:3634-3640. DOI:10.24963/ijcai.2018/505
14 SIMONOVSKY M,KOMODAKIS N.Dynamic edge-conditioned filters in convolutional neural networks on graphs[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu:IEEE,2017. DOI:10.1109/cvpr.2017.11
15 KIPF T N,WELLING M.Semi-supervised classification with graph convolutional networks[C]//The 5th International Conference on Learning Representations. Toulon:ICLR,2017.
16 SUTSKEVER I,VINYALS O,LE Q V. Sequence to sequence learning with neural networks[C]//The 27th International Conference on Neural Information Processing Systems. Cambridge,MA:NIPS,2014:3104-3112.
17 CHO K,MERRIENBOER B V,GULCEHRE C,et al.Learning phrase representations using RNN encoder-decoder for statistical machine translation[C].2014 Conference on Empirical Methods in Natural Language Processing. Doha:EMNLP,2014:1724-1734. DOI:10.3115/v1/d14-1179
18 RUMELHART D E,HINTON G E,WILLIAMS R J.Learning Internal Representations by Error Propagation[M]. Cambridge:MIT Press,1988:399-421.
19 HOCHREITER S,SCHMIDHUBER J.Long short-term memory[J].Neural Computation,1997,9(8):1735-1780.
20 WILLIAMS R J,ZIPSER D.A learning algorithm for continually running fully recurrent neural networks[J].Neural Computation,1989,1(2):270-280. DOI:10. 1162/neco.1989.1.2.270
[1] 方于华,叶枫. MFDC-Net:一种融合多尺度特征和注意力机制的乳腺癌病理图像分类算法[J]. 浙江大学学报(理学版), 2023, 50(4): 455-464.
[2] 徐圣嘉,苏程,朱孔阳,章孝灿. 基于深度学习的岩石薄片矿物自动识别方法[J]. 浙江大学学报(理学版), 2022, 49(6): 743-752.
[3] 刘华玲,张国祥,马俊. 图嵌入算法研究进展[J]. 浙江大学学报(理学版), 2022, 49(4): 443-456.
[4] 祝锦泰,叶继华,郭凤,江蕗,江爱文. FSAGN: 一种自主选择关键帧的表情识别方法[J]. 浙江大学学报(理学版), 2022, 49(2): 141-150.
[5] 杨冰, 徐丹, 张豪远, 罗海妮. 基于改进的DenseNet-BC对少数民族服饰的识别[J]. 浙江大学学报(理学版), 2021, 48(6): 676-683.
[6] 钱立辉, 王斌, 郑云飞, 章佳杰, 李马丁, 于冰. 基于图像深度预测的景深视频分类算法[J]. 浙江大学学报(理学版), 2021, 48(3): 282-288.
[7] 陈园琼, 邹北骥, 张美华, 廖望旻, 黄嘉儿, 朱承璋. 医学影像处理的深度学习可解释性研究进展[J]. 浙江大学学报(理学版), 2021, 48(1): 18-29.
[8] 李君轶, 任涛, 陆路正. 游客情感计算的文本大数据挖掘方法比较研究[J]. 浙江大学学报(理学版), 2020, 47(4): 507-520.
[9] 陈善雄, 王小龙, 韩旭, 刘云, 王明贵. 一种基于深度学习的古彝文识别方法[J]. 浙江大学学报(理学版), 2019, 46(3): 261-269.
[10] 黄婕, 张丰, 杜震洪, 刘仁义, 曹晓裴. 基于RNN-CNN集成深度学习模型的PM2.5小时浓度预测[J]. 浙江大学学报(理学版), 2019, 46(3): 370-379.
[11] 胡伟俭, 陈为, 冯浩哲, 张天平, 朱正茂, 潘巧明. 应用于平扫CT图像肺结节检测的深度学习方法综述[J]. 浙江大学学报(理学版), 2017, 44(4): 379-384.