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Volume 38 Issue 6
Aug.  2020
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Article Navigation >  ENVIRONMENTAL ENGINEERING  > 2020  >  38(6): 176-180,66
WANG Xing-run, LI Lei, YANG Xiang-hua, TIAN Yong-qiang. PROGRESS IN REMEDIATION OF CHROMIUM-CONTAMINATED SITES[J]. ENVIRONMENTAL ENGINEERING , 2020, 38(6): 1-8,23. doi: 10.13205/j.hjgc.202006001
Citation: YU Shen-ting, LIU Ping. LONG SHORT-TERM MEMORY-CONVOLUTION NEURAL NETWORK (LSTM-CNN) FOR PREDICTION OF PM2.5 CONCENTRATION IN BEIJING[J]. ENVIRONMENTAL ENGINEERING , 2020, 38(6): 176-180,66. doi: 10.13205/j.hjgc.202006029
shu

LONG SHORT-TERM MEMORY-CONVOLUTION NEURAL NETWORK (LSTM-CNN) FOR PREDICTION OF PM2.5 CONCENTRATION IN BEIJING

doi: 10.13205/j.hjgc.202006029

  • 1. China-UK Low Carbon College, Shanghai Jiao Tong University, Shanghai 200240, China;

  • 2. School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

  • Received Date: 2019-09-25
    Abstract
  • The prediction of PM2.5 can effectively prevent people from the harm by heavy pollution. However, the existing methods often emphasize the influence of local historical information and neglect the effect of spatial transport. In this paper, we proposed a method, called as long-short-term memory-convolutional neural network (LSTM-CNN), to predict PM2.5 concentration of a specific air quality monitoring station over 6 h using historical PM2.5 concentration data, historical weather data, and time stamp data. The model consisted of two parts: 1) using long-short-term memory networks to model the local variation of PM2.5 concentrations caused by local factors; 2) using one-dimensional convolutional neural networks to model the variation of PM2.5 concentrations caused by spatial transport. We randomly selected 7 monitoring stations in urban and rural areas in Beijing from May 1st 2014 to April 30th 2015 to conduct the evaluation of LSTM-CNN model. The results showed that the proposed LSTM-CNN model could provide a better prediction result than LSTM model, and a better result for monitoring stations in rural areas than those in urban areas.
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    • References(22)
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      沈阳化工大学材料科学与工程学院 沈阳 110142

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