A novel hybrid prediction model for PM2.5 concentration based on decomposition ensemble and error correction

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Abstract
PM2.5 concentration is an important index to measure the degree of air pollution. It is necessary to establish an accurate PM2.5 concentration prediction system for urban air monitoring and control. Due to the nonlinear characteristics of PM2.5 concentration, it is difficult to predict it directly. Therefore, a novel hybrid model for PM2.5 concentration based on improved variational mode decomposition (IVMD), outlier-robust extreme learning machine (ORELM) optimized by hybrid cuckoo search (CS), and chimp optimization algorithm (ChOA), error correction (EC) is proposed named IVMD-ChOACS-ORELM-EC. First of all, an improved VMD based on energy loss coefficient, named IVMD, is proposed. IVMD decomposes the original data to obtain K IMF components. Then, a hybrid optimization algorithm based on ChOA improved by CS is proposed, named ChOACS. The hybrid optimization algorithm is used to optimize ORELM. On this basis, the prediction model ChOACS-ORELM is proposed, and the K IMF components are predicted by ChOACS-ORELM. Finally, the EC model based on decomposition ensemble is established to further improve the prediction accuracy. The PM2.5 concentration data collected at hourly intervals in Beijing, Shanghai, Shenyang, and Qingdao in China are used as experimental data. The experimental results show that the correlation coefficients between the prediction results and the actual values of the four cities are 0.9999, and the prediction performance of the proposed model is better than that of all comparison models.
Funding Information
  • National Natural Science Foundation of China (No. 51709228)

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