LONG SHORT-TERM MEMORY-CONVOLUTION NEURAL NETWORK (LSTM-CNN) FOR PREDICTION OF PM<sub>2.5</sub> CONCENTRATION IN BEIJING

YU Shen-ting; LIU Ping

doi:10.13205/j.hjgc.202006029

Volume 38 Issue 6

Aug. 2020

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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

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YU Shen-ting, LIU Ping. LONG SHORT-TERM MEMORY-CONVOLUTION NEURAL NETWORK (LSTM-CNN) FOR PREDICTION OF PM_2.5 CONCENTRATION IN BEIJING[J]. ENVIRONMENTAL ENGINEERING , 2020, 38(6): 176-180,66. doi: 10.13205/j.hjgc.202006029

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LONG SHORT-TERM MEMORY-CONVOLUTION NEURAL NETWORK (LSTM-CNN) FOR PREDICTION OF PM_2.5 CONCENTRATION IN BEIJING

doi: 10.13205/j.hjgc.202006029

YU Shen-ting¹,
LIU Ping^{1,2
,
,}

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

Abstract

The prediction of PM_2.5 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 PM_2.5 concentration of a specific air quality monitoring station over 6 h using historical PM_2.5 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 PM_2.5 concentrations caused by local factors; 2) using one-dimensional convolutional neural networks to model the variation of PM_2.5 concentrations caused by spatial transport. We randomly selected 7 monitoring stations in urban and rural areas in Beijing from May 1^st 2014 to April 30^th 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.
- LSTM,
- CNN,
- prediction of PM_2.5 concentration,
- deep learning,
- spatiotemporal data

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