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PM2.5 concentration prediction based on EEMD-Stacking - A case study of Yangtze River Delta, China

Published: 17 September 2022 Publication History

Abstract

With the acceleration of China's industrialization process, the resulting environmental problems have become increasingly prominent, especially the rising concentration of PM2.5 in the air, which has caused various consequences for people's clothing, food, housing and transportation. Due to the randomness and complexity of PM2.5 concentration time series, this paper uses EEMD to decompose the historical PM2.5 concentration data into EIMF and trend series. Considering air quality factors and meteorological factors, this paper constructs EEMD-Stacking model, and uses Bayesian algorithm to optimize the parameters. The Yangtze River Delta region was selected as the experimental site, and the daily PM2.5 concentration data and meteorological station data from 2018 to 2020 were used for prediction experiments. The results show that the combined model has good prediction effect. The short-term prediction accuracy is relatively high, and the medium and long-term prediction accuracy decreases, but the overall prediction accuracy is high and stable.

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          cover image ACM Other conferences
          ICSLT '22: Proceedings of the 8th International Conference on e-Society, e-Learning and e-Technologies
          June 2022
          125 pages
          ISBN:9781450396660
          DOI:10.1145/3545922
          © 2022 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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          New York, NY, United States

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          Published: 17 September 2022

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          1. PM2.5,EEMD,Stacking,Bayesian optimization

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