电子学报 ›› 2022, Vol. 50 ›› Issue (6): 1410-1427.DOI: 10.12263/DZXB.20200258

• 学术论文 • 上一篇    下一篇

一种卷积自编码深度学习的空气污染多站点联合预测模型

张波1,3, 陆云杰1, 秦东明2,4, 邹国建1   

  1. 1.上海师范大学信息与机电工程学院,上海 200234
    2.同济大学电子与信息工程学院,上海 201804
    3.上海智能教育大数据工程技术研究中心,上海 200234
    4.中科三清科技有限公司,北京 100089
  • 收稿日期:2020-03-14 修回日期:2021-07-25 出版日期:2022-06-25 发布日期:2022-06-25
  • 作者简介:张 波 男,1979年11月出生,江苏常州人.同济大学计算机应用技术专业工学博士.现为上海师范大学信息与机电工程学院院长、教授.主要研究方向为新一代人工智能与深度学习技术等.E-mail: zhangbo@shnu.edu.cn
    陆云杰 男,1995年6月出生,江苏苏州人.上海师范大学信息与机电工程学院计算机应用技术工学硕士.主要研究方向为机器学习.E-mail: 1000459475@smail.shnu.edu.cn
    秦东明 男,1981年4月出生,河南社旗人.同济大学电子与信息工程学院工学博士.主要研究方向为高性能计算、人工智能、深度学习等.
    邹国建 男,1993年11月出生,安徽滁州人.上海师范大学信息与机电工程学院计算机应用技术专业工学硕士.主要研究方向为深度学习、知识图谱.
  • 基金资助:
    国家自然科学基金(61802258);上海自然科学基金(18ZR1428300)

A Multi-Site Joint Air Pollution Prediction Model Based on Convolutional Auto-Encoder Deep Learning

ZHANG Bo1,3, LU Yun-jie1, QIN Dong-ming2,4, ZOU Guo-jian1   

  1. 1.College of Information,Mechanical and Electrical Engineering,Shanghai Normal University,Shanghai 200234,China
    2.College of Electronic and Information Engineering,Tongji University,Shanghai 201804,China
    3.Shanghai Engineering Research Center of Intelligent Education and Bigdata,Shanghai Normal University,Shanghai 200234,China
    4.3Clear Science & Technology Co. Ltd,Beijing 100193,China
  • Received:2020-03-14 Revised:2021-07-25 Online:2022-06-25 Published:2022-06-25

摘要:

城市空气污染因空间扩散特性呈现出区域内的浓度高关联性.因此如何通过多个空气污染监测站的时空数据预测特定目标地点的污染情况,以解决站点分布不匀的问题,是一个重要的研究工作.本文结合空气污染物因素特性和气象因素的多维度影响,提出了一个利用区域内多站点空间监测数据实现特定目标站点的空气污染物浓度预测模型.该模型通过多层卷积神经网络(Convolutional Neural Network,CNN)实现城市多站点污染物浓度与气象数据之间的维度关联特征及空间关联特征学习,进而利用基于多层长短期记忆网络(Long Short-Term Memory,LSTM)的自编码网络实现多站点浓度的时序关联特征分析.实验通过真实数据集验证,所提出的预测模型获得了高于传统机器学习污染物浓度预测模型的预测准确度,且在多个城市数据集上验证了模型的泛化能力.

关键词: 深度学习, 空气污染, 时空数据, 多站点联合预测, 卷积神经网络, 长短期记忆网络

Abstract:

Due to the nature of spatial diffusion, urban air pollution presents a high correlation with regional concentration feature. Therefore, how to use spatio-temporal related data from urban multiple air pollution detection sites to predict air pollution concentration of a special target location is an important research effort for solving the problem of uneven site distribution. Concerning the multi-dimensional impacts of the air pollutant factors’ features and the influence of meteorological factors, we propose an air pollutant concentration prediction model, which uses multi-site spatial detection data within a region to predict the concentration of the target station in this paper. This model is able to learn dimensional correlation characteristics and spatial correlation characteristics from multi-site pollutant concentration and meteorological data in the urban area through the multi-layer convolutional neural network, and then analyze the time-series correlation characteristics of multi-site concentration by utilizing the multi-layer auto-encoder network based long short-term memory network. The experimental results show that our proposed model obtains better performances than traditional machine learning based models under a real-world dataset, and meanwhile, the generalization performance of the proposed model has been examined based on multiple urban air pollution database.

Key words: deep learning, air pollution, spatio-temporal data, multiple sites joint prediction, convolutional neural network, long short-term memory network

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