A hybrid air pollutant concentration prediction model combining secondary decomposition and sequence reconstruction. (November 2020)

• Record Type: Journal Article
• Authors: Sun, Wei; Huang, Chenchen
• Is Part Of: Environmental pollution. Volume 266:Part 3(2020)
• Publication Date: 2020-11
• Subjects: Air pollutant prediction -- Extreme learning machine -- Whale optimization algorithm -- Sample entropy -- Complete ensemble empirical mode decomposition with adaptive noise -- Acid rain; Pollution -- Periodicals; Pollution -- Environmental aspects -- Periodicals; Environmental Pollution -- Periodicals; Pollution -- Périodiques; Pollution -- Aspect de l'environnement -- Périodiques; Pollution -- Effets physiologiques -- Périodiques; Pollution; Pollution -- Environmental aspects; Periodicals; Electronic journals; 363.73
• DOI: 10.1016/j.envpol.2020.115216
• Languages: English
• ISSNs: 0269-7491

Abstract

Abstract: Acid rain is a serious threat to terrestrial ecosystems. To provide more accurate early warning information for acid rain prevention, urban planning, and travel planning, a novel air pollutant prediction model was proposed in this paper to predict NO2 and SO2 . First, the data were decomposed into several sub-sequences by a complete ensemble empirical mode decomposition with adaptive noise. Second, the subsequences are reconstructed by variational mode decomposition and sample entropy. Then, the new subsequences are predicted by the extreme learning machine combined with the whale optimization algorithm. The empirical analysis was carried out through 8 data sets. According to the experimental results, three main conclusions can be drawn. First, the proposed model in this paper has excellent prediction performance and robustness. In all the comparison experiments, the R 2 and RMSE of the proposed model are the best among all the models. Second, data preprocessing is very necessary. After adding the decomposition algorithm, the average improvement levels of R 2 and RMSE were 897.57% and 50.78%, respectively. Third, the re-decomposition of IMF1 is an effective method to improve prediction accuracy. After the re-decomposition of IMF1, R 2 can be improved by 13.64% on average on the original basis, and RMSE can be reduced by 31.99% on average. The results of this study can provide a valuable reference for the research of air pollutant prediction. In future work, the application of the proposed model in other air pollutants or other regions can be explored. Graphical abstract: Image 1 Highlights: Whale optimization algorithm can better optimize the extreme learning machine. Secondary decomposition break through the limitation of the decomposition algorithm. The first intrinsic mode function has the highest sample entropy. Signal processing technology can improve the accuracy of prediction.