The application of deep learning method in Shanghai PM2.5 prediction

被引:0
|
作者
深度学习方法在上海市PM2.5浓度预报中的应用
机构
[1] [1,2,Ma, Jing-Hui
[2] 1,Cao, Yu
[3] 1,Yu, Zhong-Qi
[4] 1,Qu, Yuan-Hao
[5] 1,Xu, Jian-Ming
来源
Cao, Yu (liushuicaoyu@163.com) | 1600年 / Chinese Society for Environmental Sciences卷 / 40期
关键词
D O I
暂无
中图分类号
学科分类号
摘要
引用
收藏
页码:530 / 538
相关论文
共 50 条
  • [1] Application of the XGBoost Machine Learning Method in PM2.5 Prediction: A Case Study of Shanghai
    Ma, Jinghui
    Yu, Zhongqi
    Qu, Yuanhao
    Xu, Jianming
    Cao, Yu
    AEROSOL AND AIR QUALITY RESEARCH, 2020, 20 (01) : 128 - 138
  • [2] A deep learning model for PM2.5 concentration prediction
    Zhang, Zhendong
    Ma, Xiang
    Yan, Ke
    2021 IEEE INTL CONF ON DEPENDABLE, AUTONOMIC AND SECURE COMPUTING, INTL CONF ON PERVASIVE INTELLIGENCE AND COMPUTING, INTL CONF ON CLOUD AND BIG DATA COMPUTING, INTL CONF ON CYBER SCIENCE AND TECHNOLOGY CONGRESS DASC/PICOM/CBDCOM/CYBERSCITECH 2021, 2021, : 428 - 433
  • [3] Prediction of PM2.5 concentration in Ulaanbaatar with deep learning models
    Suriya
    Natsagdorj, Narantsogt
    Aorigele
    Zhou, Haijun
    Sachurila
    URBAN CLIMATE, 2023, 47
  • [4] A spatiotemporal XGBoost model for PM2.5 concentration prediction and its application in Shanghai
    Wang, Zidong
    Wu, Xianhua
    Wu, You
    HELIYON, 2023, 9 (12)
  • [5] Deep-learning architecture for PM2.5 concentration prediction: A review
    Zhou, Shiyun
    Wang, Wei
    Zhu, Long
    Qiao, Qi
    Kang, Yulin
    ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY, 2024, 21
  • [6] Citywide PM2.5 Concentration Prediction Using Deep Learning Model
    Yang, Xiaonuo
    Sun, Xiao
    Liu, Na
    Chai, Yueting
    2024 IEEE 4TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND ARTIFICIAL INTELLIGENCE, SEAI 2024, 2024, : 247 - 251
  • [7] PM2.5 CONCENTRATION PREDICTION USING DEEP LEARNING IN AIR MONITORING
    Huang, Yi
    FRESENIUS ENVIRONMENTAL BULLETIN, 2021, 30 (12): : 13200 - 13211
  • [8] A PM2.5 prediction model based on deep learning and random forest
    Peng H.
    Zhou Y.
    Hu X.
    Zhang L.
    Peng Y.
    Cai X.
    National Remote Sensing Bulletin, 2023, 27 (02) : 430 - 440
  • [9] A deep learning-based hybrid method for PM2.5 prediction in central and western China
    Zuhan Liu
    Zihai Fang
    Yuanhao Hu
    Scientific Reports, 15 (1)
  • [10] Short-term prediction of PM2.5 pollution with deep learning methods
    Ayturan, Y. A.
    Ayturan, Z. C.
    Altun, H. O.
    Kongoli, C.
    Tuncez, F. D.
    Dursun, S.
    Ozturk, A.
    GLOBAL NEST JOURNAL, 2020, 22 (01): : 126 - 131