Deep Hybrid Model Based on EMD with Classification by Frequency Characteristics for Long-Term Air Quality Prediction

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ID: 267861
2020
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Abstract
Air pollution (mainly PM2.5) is one of the main environmental problems about air quality. Air pollution prediction and early warning is a prerequisite for air pollution prevention and control. However, it is not easy to accurately predict the long-term trend because the collected PM2.5 data have complex nonlinearity with multiple components of different frequency characteristics. This study proposes a hybrid deep learning predictor, in which the PM2.5 data are decomposed into components by empirical mode decomposition (EMD) firstly, and a convolutional neural network (CNN) is built to classify all the components into a fixed number of groups based on the frequency characteristics. Then, a gated-recurrent-unit (GRU) network is trained for each group as the sub-predictor, and the results from the three GRUs are fused to obtain the prediction result. Experiments based on the PM2.5 data from Beijing verify the proposed model, and the prediction results show that the decomposition and classification can develop the accuracy of the proposed predictor for air pollution prediction greatly.
Reference Key
jin2020mathematicsdeep Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Xue-Bo Jin;Nian-Xiang Yang;Xiao-Yi Wang;Yu-Ting Bai;Ting-Li Su;Jian-Lei Kong;Jin, Xue-Bo;Yang, Nian-Xiang;Wang, Xiao-Yi;Bai, Yu-Ting;Su, Ting-Li;Kong, Jian-Lei;
Journal Mathematics
Year 2020
DOI
10.3390/math8020214
URL
https://doi.org/10.3390/math8020214
Keywords
cnn gru emd pm2.5 data air pollution prediction

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