Academic Journal of Computing & Information Science, 2021, 4(5); doi: 10.25236/AJCIS.2021.040502.
Kexin Yan1, Jiaxun Liang2
1School of Mathematics and Statistics, Ningbo University, Ningbo, Zhejiang, 315211, China
2School of Quality and Technical Supervision, Hebei University, Baoding, Hebei, 071002, China
Aiming at the problem of predicting AQI, this paper proposes a hybrid prediction model. The specific method is to use Complementary Ensemble Empirical Mode Decomposition (CEEMD) to preprocess the non-stationary sequence, and then use the Elman neural network optimized by WOA to predict. The construction of CEEMD-WOA-Elman model mainly includes four parts: preprocessing, optimization, prediction, and evaluation. In order to evaluate the effectiveness and generalization ability of the model, we introduced four evaluation indicators to comprehensively evaluate the prediction model proposed in this paper. The analysis results show that compared with other models, the hybrid prediction model proposed in this paper has higher prediction accuracy and the predicted results obtained are more excellent.
complementary ensemble empirical mode decomposition, whale optimization algorithm, Elman neural network, air quality index prediction, CEEMD-WOA-Elman hybrid model
Kexin Yan, Jiaxun Liang. AQI prediction based on CEEMD-WOA-Elman neural network. Academic Journal of Computing & Information Science (2021), Vol. 4, Issue 5: 8-15. https://doi.org/10.25236/AJCIS.2021.040502.
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