School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an, 710121, Shaanxi, China.
Environ Sci Pollut Res Int. 2023 Mar;30(15):44893-44913. doi: 10.1007/s11356-023-25238-8. Epub 2023 Jan 26.
PM concentration is an important index to measure the degree of air pollution. It is necessary to establish an accurate PM concentration prediction system for urban air monitoring and control. Due to the nonlinear characteristics of PM concentration, it is difficult to predict it directly. Therefore, a novel hybrid model for PM 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 PM 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.
PM 浓度是衡量空气污染程度的一个重要指标。为了进行城市空气监测和控制，有必要建立一个准确的 PM 浓度预测系统。由于 PM 浓度具有非线性的特点，直接进行预测较为困难。因此，提出了一种新的基于改进变分模态分解（IVMD）、混合布谷鸟搜索（CS）优化的鲁棒异常值极端学习机（ORELM）和黑猩猩优化算法（ChOA）、误差修正（EC）的 PM 浓度混合模型，命名为 IVMD-ChOACS-ORELM-EC。首先，提出了一种基于能量损耗系数的改进 VMD，命名为 IVMD。IVMD 将原始数据分解为 K 个 IMF 分量。然后，提出了一种基于 CS 改进的 ChOA 的混合优化算法，命名为 ChOACS。混合优化算法用于优化 ORELM。在此基础上，提出了预测模型 ChOACS-ORELM，并通过 ChOACS-ORELM 对 K 个 IMF 分量进行预测。最后，建立了基于分解集成的 EC 模型，进一步提高了预测精度。实验采用了中国北京、上海、沈阳和青岛四个城市的每小时间隔采集的 PM 浓度数据作为实验数据。实验结果表明，四个城市的预测结果与实际值的相关系数均为 0.9999，表明所提出模型的预测性能优于所有对比模型。
