A Hybrid Model for PM 2.5 Concentration Forecasting Based on Neighbor Structural Information, a Case in North China

Ping Wang, Xuran He, Hongyinping Feng, Guisheng Zhang and Chenglu Rong
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Ping Wang: College of Resources and Environment, Shanxi University of Finance and Economics, Taiyuan 030006, China
Xuran He: School of Cybersecurity, Northwestern Polytechnical University, Xi’an 710129, China
Hongyinping Feng: School of Mathematical Sciences, Shanxi University, Taiyuan 030006, China
Guisheng Zhang: School of Economics and Management, Shanxi University, Taiyuan 030006, China
Chenglu Rong: College of Resources and Environment, Shanxi University of Finance and Economics, Taiyuan 030006, China

Sustainability, 2021, vol. 13, issue 2, 1-19

Abstract: PM 2.5 concentration prediction is an important task in atmospheric environment research, so many prediction models have been established, such as machine learning algorithm, which shows remarkable generalization ability. The time series data composed of PM 2.5 concentration have the implied structural characteristics such as the sequence characteristic in time dimension and the high dimension characteristic in dynamic-mode space, which makes it different from other research data. However, when the machine learning algorithm is applied to the PM 2.5 time series prediction, due to the principle of input data composition, the above structural characteristics can not be fully reflected. In our study, a neighbor structural information extraction algorithm based on dynamic decomposition is proposed to represent the structural characteristics of time series, and a new hybrid prediction system is established by using the extracted neighbor structural information to improve the accuracy of PM 2.5 concentration prediction. During the process of extracting neighbor structural information, the original PM 2.5 concentration series is decomposed into finite dynamic modes according to the neighborhood data, which reflects the time series structural characteristics. The hybrid model integrates the neighbor structural information in the form of input vector, which ensures the applicability of the neighbor structural information and retains the composition form the original prediction system. The experimental results of six cities show that the hybrid prediction systems integrating neighbor structural information are significantly superior to the traditional models, and also confirm that the neighbor structural information extraction algorithm can capture effective time series structural information.

Keywords: PM 2.5 concentration prediction; dynamic decomposition; neighbor structural information extraction; hybrid prediction model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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