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

Xue-Bo Jin, Nian-Xiang Yang, Xiao-Yi Wang, Yu-Ting Bai, Ting-Li Su and Jian-Lei Kong
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Xue-Bo Jin: School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
Nian-Xiang Yang: School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
Xiao-Yi Wang: School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
Yu-Ting Bai: School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
Ting-Li Su: School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
Jian-Lei Kong: School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China

Mathematics, 2020, vol. 8, issue 2, 1-17

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.

Keywords: PM2.5 data; air pollution prediction; EMD; CNN; GRU (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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