CEEMD-LASSO-ELM nonlinear combined model of air quality index prediction for four cities in China

Air pollution prevention and control is an important way to eliminate haze, and air quality prediction can provide predictive information for air pollution prevention and people’s health. Therefore, it is of great practical usefulness to establish a scientific and effective air quality prediction mo...

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Bibliographic Details
Published in:Environmental and ecological statistics 2023-09, Vol.30 (3), p.309-334
Main Authors: Zhu, Suling, Wang, Peiqi, Wang, Ruyi, Li, Min, Che, Jinxing, Wang, Xinlu
Format: Article
Language:English
Subjects:
Air pollution
Air pollution forecasting
Air quality
Biomedical and Life Sciences
Chemistry and Earth Sciences
Collinearity
Computer Science
Data analysis
Ecology
Forecasting
Haze
Health Sciences
Life Sciences
Math. Appl. in Environmental Science
Mathematical models
Medicine
Outdoor air quality
Physics
Pollution abatement
Pollution control
Pollution prevention
Prediction models
Predictions
Statistical methods
Statistics for Engineering
Statistics for Life Sciences
Theoretical Ecology/Statistics
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Summary:Air pollution prevention and control is an important way to eliminate haze, and air quality prediction can provide predictive information for air pollution prevention and people’s health. Therefore, it is of great practical usefulness to establish a scientific and effective air quality prediction model. Combined forecasting is a popular statistical method for air pollution forecasting, which generally includes two steps of individual model selection and weight determination. This research introduces LASSO for individual models selection, and ELM to establish a nonlinear combined forecasting model, named CEEMD-LASSO-ELM. LASSO can not only select individual models, but also avoids the possible collinearity between the selected individual models. Daily air quality index (AQI) series of Guangzhou, Kunming, Lanzhou and Hulunbuir are selected to verify the feasibility and effectiveness of the proposed CEEMD-LASSO-ELM model. Combined with CEEMD, seven comparative models are Random-ELM, Rank-ELM, Random-LR, Rank-LR, LASSO-GRNN, LASSO-PSOGSASVR and LASSO-LR. The data analysis shows that the proposed CEEMD-LASSO-ELM model has better prediction accuracy and stronger generalization ability. Taking Lanzhou as an example, the MAPE of CEEMD-LASSO-ELM model is 1.813% lower than that of the seven comparative models on average. Graphical abstract
ISSN:1352-8505
1573-3009
DOI:10.1007/s10651-023-00562-x