Including the feature of appropriate adjacent sites improves the PM2.5 concentration prediction with long short-term memory neural network model

被引：0
|
作者：
Mengfan, Teng [1 ]
Siwei, Li [1 ,2 ]
ge, Song [1 ]
jie, Yang [1 ,2 ]
Lechao, Dong [1 ]
hao, Lin [1 ]
Senlin, Hu [1 ]
机构：
[1] School of Remote Sensing and Information Engineering, Wuhan University, Wuhan,430079, China
[2] State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan,430079, China
来源：
Sustainable Cities and Society | 2022年 / 76卷
关键词：
Engineering Village;
D O I：
103427
中图分类号：
学科分类号：
摘要：
Adjacent sites - Appropriate adjacent site - Concentration prediction - Convolutional neural network - Hybrid model - Long short-term memory neural network - Neural network model - Neural-networks - PM 2.5 - Policy makers
引用
收藏
相关论文
共 50 条
  • [41] Analysis and Prediction of PM2.5 Pollution in Madrid: The Use of Prophet-Long Short-Term Memory Hybrid Models
    Caceres-Tello, Jesus
    Galan-Hernandez, Jose Javier
    APPLIEDMATH, 2024, 4 (04): : 1428 - 1452
  • [42] 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
  • [43] Stock Prediction Based on Genetic Algorithm Feature Selection and Long Short-Term Memory Neural Network
    Chen, Shile
    Zhou, Changjun
    IEEE ACCESS, 2021, 9 : 9066 - 9072
  • [44] Research on PM2.5 concentration prediction algorithm based on graph convolutional neural network model
    Liu, Xiangyu
    Ren, Ge
    Guo, Jiashuo
    Hu, Yuxin
    Lin, Hong
    Proceedings of SPIE - The International Society for Optical Engineering, 2024, 13291
  • [45] Short-Term Passenger Flow Prediction Using a Bus Network Graph Convolutional Long Short-Term Memory Neural Network Model
    Baghbani, Asiye
    Bouguila, Nizar
    Patterson, Zachary
    TRANSPORTATION RESEARCH RECORD, 2023, 2677 (02) : 1331 - 1340
  • [46] Forecasting daily PM2.5 concentrations in Wuhan with a spatial-autocorrelation-based long short-term memory model
    Liu, Zhifei
    Ge, Chengjun
    Zheng, Kang
    Bao, Shuai
    Cui, Yide
    Yuan, Yirong
    Zhang, Yixuan
    ATMOSPHERIC ENVIRONMENT, 2024, 331
  • [47] Prediction of PM2.5 Daily Concentration of Guangzhou Based on Neural Network Algorithms
    Li Z.
    Wei J.
    Beijing Daxue Xuebao (Ziran Kexue Ban)/Acta Scientiarum Naturalium Universitatis Pekinensis, 2021, 57 (04): : 645 - 652
  • [48] Sea surface temperature prediction model based on long and short-term memory neural network
    Li, Xiaojing
    3RD INTERNATIONAL FORUM ON GEOSCIENCE AND GEODESY, 2021, 658
  • [49] Long Short-Term Memory Neural Network Applied to Train Dynamic Model and Speed Prediction
    Li, Zhen
    Tang, Tao
    Gao, Chunhai
    ALGORITHMS, 2019, 12 (08)
  • [50] An Evaporation Duct Height Prediction Model Based on a Long Short-Term Memory Neural Network
    Zhao, Wenpeng
    Zhao, Jun
    Li, Jincai
    Zhao, Dandan
    Huang, Lilan
    Zhu, Junxing
    Lu, Jingze
    Wang, Xiang
    IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, 2021, 69 (11) : 7795 - 7804
12345