Xiaorui Shao, Chang Soo Kim*
CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5143-5160, 2022, DOI:10.32604/cmc.2022.020689
- 11 October 2021
Abstract
Accurate multi-step PM2.5 (particulate matter with diameters ≤2.5um) concentration prediction is critical for humankinds’ health and air population management because it could provide strong evidence for decision-making. However, it is very challenging due to its randomness and variability. This paper proposed a novel method based on convolutional neural network (CNN) and long-short-term memory (LSTM) with a space-shared mechanism, named space-shared CNN-LSTM (SCNN-LSTM) for multi-site daily-ahead multi-step PM2.5 forecasting with self-historical series. The proposed SCNN-LSTM contains multi-channel inputs, each channel corresponding to one-site historical PM2.5 concentration series. In which, CNN and LSTM are used to
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