Medium and long-term trend prediction of urban air quality based on deep learning

被引:2
|
作者
Wang, Zhencheng [1 ,2 ]
Xie, Feng [3 ]
机构
[1] Guangxi Univ, Sch Comp Elect & Informat, Nanning 530004, Peoples R China
[2] Guangxi Univ, Guangxi Key Lab Multimedia Commun & Network Techn, Nanning 530004, Peoples R China
[3] Guangxi Vocat Coll Safety Engn, Nanning 530100, Peoples R China
关键词
deep learning; air quality; meteorological characteristics; forecast effect; air quality index; AQI; NEURAL-NETWORK; POLLUTION;
D O I
10.1504/IJETM.2022.120724
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
In order to overcome the low accuracy of traditional air quality change prediction methods, this paper designs a medium and long-term prediction method of urban air quality change trend based on deep learning. The deep learning network is constructed, and the air quality prediction process is designed by using the deep learning algorithm to optimise the air quality prediction model. The deep belief network is initialised by unsupervised training, and the data is supervised by back propagation algorithm. By continuously optimising the network weights to avoid the network falling into local optimum, the medium and long-term accurate prediction of air quality change trend can be realised. The experimental results show the AQI index value of the prediction results of the model has a high fitting degree with the actual value, and the evaluation values of RMSE, MAE, MSE and SMAPE are 2.608%, 2.613%, 2.07% and 0.9513 respectively, which proves the effectiveness of the method.
引用
收藏
页码:22 / 37
页数:16
相关论文
共 50 条
  • [1] Air quality prediction and long-term trend analysis: a case study of Beijing
    Liu, B.
    Wang, M.
    Hu, Z.
    Shi, C.
    Li, J.
    Qu, G.
    INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY, 2023, 20 (07) : 7911 - 7924
  • [2] Long-Term Urban Traffic Speed Prediction With Deep Learning on Graphs
    Yu, James J. Q.
    Markos, Christos
    Zhang, Shiyao
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (07) : 7359 - 7370
  • [3] Air quality prediction and long-term trend analysis: a case study of Beijing
    B. Liu
    M. Wang
    Z. Hu
    C. Shi
    J. Li
    G. Qu
    International Journal of Environmental Science and Technology, 2023, 20 : 7911 - 7924
  • [4] Long-term trend prediction of pandemic combining the compartmental and deep learning models
    Chen, Wanghu
    Luo, Heng
    Li, Jing
    Chi, Jiacheng
    SCIENTIFIC REPORTS, 2024, 14 (01):
  • [5] A Deep Learning Based Approach for Long-Term Drought Prediction
    Agana, Norbert A.
    Homaifar, Abdollah
    SOUTHEASTCON 2017, 2017,
  • [6] Deep Hybrid Model Based on EMD with Classification by Frequency Characteristics for Long-Term Air Quality Prediction
    Jin, Xue-Bo
    Yang, Nian-Xiang
    Wang, Xiao-Yi
    Bai, Yu-Ting
    Su, Ting-Li
    Kong, Jian-Lei
    MATHEMATICS, 2020, 8 (02)
  • [7] Computational deep air quality prediction techniques: a systematic review
    Kaur, Manjit
    Singh, Dilbag
    Jabarulla, Mohamed Yaseen
    Kumar, Vijay
    Kang, Jusung
    Lee, Heung-No
    ARTIFICIAL INTELLIGENCE REVIEW, 2023, 56 (SUPPL 2) : 2053 - 2098
  • [8] PREDICTION OF POLLUTANT DIFFUSION TREND USING DEEP LEARNING IN AIR QUALITY MONITORING BIG DATA
    Yuan, Minglan
    Wang, Zhizhong
    FRESENIUS ENVIRONMENTAL BULLETIN, 2022, 31 (03): : 2942 - 2953
  • [9] Long-term Tracking Based on Deep Learning
    Wu, Ming
    Zhang, Chuang
    Sun, Zhongkai
    Li, Xiaoqi
    PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON UBIQUITOUS INFORMATION MANAGEMENT AND COMMUNICATION (IMCOM 2018), 2018,
  • [10] EvaNet: An Extreme Value Attention Network for Long-Term Air Quality Prediction
    Chen, Zechuan
    Yu, Haomin
    Geng, Yangli-ao
    Li, Qingyong
    Zhang, Yingjun
    2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2020, : 4545 - 4552