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

by Xue-Bo Jin 1,2,3,*, Nian-Xiang Yang 1,2,3, Xiao-Yi Wang 1,2,3,*, Yu-Ting Bai 1,2,3, Ting-Li Su 1,2,3 and Jian-Lei Kong 1,2,3
1
School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
2
China Light Industry Key Laboratory of Industrial Internet and Big Data, Beijing Technology and Business University, Beijing 100048, China
3
Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
*
Authors to whom correspondence should be addressed.
Mathematics 2020, 8(2), 214; https://doi.org/10.3390/math8020214
Received: 10 January 2020 / Revised: 3 February 2020 / Accepted: 5 February 2020 / Published: 7 February 2020
(This article belongs to the Special Issue Advances in Prediction Models)
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. View Full-Text
Keywords: PM2.5 data; air pollution prediction; EMD; CNN; GRU PM2.5 data; air pollution prediction; EMD; CNN; GRU
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Jin, X.-B.; Yang, N.-X.; Wang, X.-Y.; Bai, Y.-T.; Su, T.-L.; Kong, J.-L. Deep Hybrid Model Based on EMD with Classification by Frequency Characteristics for Long-Term Air Quality Prediction. Mathematics 2020, 8, 214.

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