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An ensemble multi-scale framework for long-term forecasting of air qualityopen access

Authors
Jiang, ShanYu, Zu-GuoAnh, Vo V.Lee, TaesamZhou, Yu
Issue Date
Jan-2024
Publisher
AIP Publishing
Citation
CHAOS, v.34, no.1
Indexed
SCIE
SCOPUS
Journal Title
CHAOS
Volume
34
Number
1
URI
https://scholarworks.gnu.ac.kr/handle/sw.gnu/69442
DOI
10.1063/5.0172382
ISSN
1054-1500
1089-7682
Abstract
The significance of accurate long-term forecasting of air quality for a long-term policy decision for controlling air pollution and for evaluating its impacts on human health has attracted greater attention recently. This paper proposes an ensemble multi-scale framework to refine the previous version with ensemble empirical mode decomposition (EMD) and nonstationary oscillation resampling (NSOR) for long-term forecasting. Within the proposed ensemble multi-scale framework, we on one hand apply modified EMD to produce more regular and stable EMD components, allowing the long-range oscillation characteristics of the original time series to be better captured. On the other hand, we provide an ensemble mechanism to alleviate the error propagation problem in forecasts caused by iterative implementation of NSOR at all lead times and name it improved NSOR. Application of the proposed multi-scale framework to long-term forecasting of the daily PM2.5 at 14 monitoring stations in Hong Kong demonstrates that it can effectively capture the long-term variation in air pollution processes and significantly increase the forecasting performance. Specifically, the framework can, respectively, reduce the average root-mean-square error and the mean absolute error over all 14 stations by 8.4% and 9.2% for a lead time of 100 days, compared to previous studies. Additionally, better robustness can be obtained by the proposed ensemble framework for 180-day and 365-day long-term forecasting scenarios. It should be emphasized that the proposed ensemble multi-scale framework is a feasible framework, which is applicable for long-term time series forecasting in general.
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