A New Decomposition Ensemble Learning Approach with Intelligent Optimization for PM2.5 Concentration Forecasting
Joint Authors
Sun, Shaolong
Guo, Ju’e
Xing, Guangyuan
Source
Discrete Dynamics in Nature and Society
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-03-22
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
In this study, we focus our attention on the forecasting of daily PM2.5 concentrations.
According to the principle of “divide and conquer,” we propose a novel decomposition ensemble learning approach by integrating ensemble empirical mode decomposition (EEMD), artificial neural networks (ANNs), and adaptive particle swarm optimization (APSO) for forecasting PM2.5 concentrations.
Our proposed decomposition ensemble learning approach is formulated exclusively to deal with difficulties in quantitating meteorological information with high volatility, irregularity, and complicacy.
This decomposition ensemble learning approach mainly consists of three steps.
First, we utilize EEMD to decompose original time series of PM2.5 concentrations into a specific amount of independent intrinsic mode functions (IMFs) and residual term.
Second, the ANN, whose connection parameters are optimized by APSO algorithm, is employed to model IMFs and residual terms, respectively.
Finally, another APSO-ANN is applied to aggregate the forecast IMFs and residual term into a collection as the final forecasting results.
The empirical results show that the forecasting of our decomposition ensemble learning approach outperforms other benchmark models in terms of level accuracy and directional accuracy.
American Psychological Association (APA)
Xing, Guangyuan& Sun, Shaolong& Guo, Ju’e. 2020. A New Decomposition Ensemble Learning Approach with Intelligent Optimization for PM2.5 Concentration Forecasting. Discrete Dynamics in Nature and Society،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1153232
Modern Language Association (MLA)
Xing, Guangyuan…[et al.]. A New Decomposition Ensemble Learning Approach with Intelligent Optimization for PM2.5 Concentration Forecasting. Discrete Dynamics in Nature and Society No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1153232
American Medical Association (AMA)
Xing, Guangyuan& Sun, Shaolong& Guo, Ju’e. A New Decomposition Ensemble Learning Approach with Intelligent Optimization for PM2.5 Concentration Forecasting. Discrete Dynamics in Nature and Society. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1153232
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1153232