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 R² 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 R² 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, R² 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.

酸雨是对陆地生态系统的严重威胁。为了为酸雨的预防，城市规划和出行规划提供更准确的预警信息，本文提出了一种新型的空气污染物预测模型来预测NO ₂和SO _2。。首先，通过具有自适应噪声的完整整体经验模式分解，将数据分解为几个子序列。其次，通过变分模式分解和样本熵重构子序列。然后，极限学习机结合鲸鱼优化算法对新的子序列进行预测。通过8个数据集进行了实证分析。根据实验结果，可以得出三个主要结论。首先，本文提出的模型具有出色的预测性能和鲁棒性。在所有比较实验中，所提出模型的R ²和RMSE是所有模型中最好的。其次，数据预处理是非常必要的。添加分解算法后，R的平均改进水平²和RMSE分别为897.57％和50.78％。第三，IMF1的重新分解是提高预测精度的有效方法。IMF1分解后，R ²可以平均提高13.64％，RMSE可以平均降低31.99％。研究结果可为大气污染物预测研究提供有价值的参考。在未来的工作中，可以探索该模型在其他空气污染物或其他地区的应用。